Publications

YEARS
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2024

R. Teehan, B. Lake, and M. Ren. (2024), “CoLLEGe: Concept embedding generation for large language models”, arXiv preprint arXiv:2403.15362

A. E. Orhan, W. Wang, A. N. Wang, M. Ren, and B. M. Lake. (2024), “Self-supervised learning of video representations from a child’s perspective”, arXiv preprint arXiv:2402.00300

A. N. Wang, C. Hoang, Y. Xiong, Y. LeCun, and M. Ren. (2024), “PooDLe: Pooled and dense self-supervised learning from naturalistic videos”, arXiv preprint arXiv:2408.11208

J. Lu, R. Teehan, and M. Ren. (2024), “ProCreate, don’t reproduce! Propulsive energy diffusion for creative generation”, arXiv preprint arXiv:2408.02226

Y. Zhang, L. Charlin, R. Zemel, and M. Ren. (2024), “Integrating present and past in unsupervised continual learning”, arXiv preprint arXiv:2404.19132

Y. Yang, M. Jones, M. C. Mozer, and M. Ren. (2024), “Reawakening knowledge: Anticipatory recovery from catastrophic interference via structured training”, arXiv preprint arXiv:2403.09613

A. E. Orhan, W. Wang, A. N. Wang, M. Ren, and B. M. Lake. (2024), “Self-supervised learning of video representations from a child’s perspective”, arXiv e-prints, pages arXiv: 2402.00300

R. Tibrewala, T. Dutt, A. Tong, L. Ginocchio, R. Lattanzi, M. B. Keerthivasan, S. H. Baete, S. Chopra, Y. W. Lui, D. K. Sodickson, H. Chandarana, and P. M. Johnson. (2024), “FastMRI Prostate: A public, biparametric MRI dataset to advance machine learning for prostate cancer imaging”, Scientific Data, Vol. 11, pages 404

H. Huang, S. Rawlekar, S. Chopra, and C. M. Deniz. (2024), “Radiology Reports Improve Visual Representations Learned from Radiographs”, Medical Imaging with Deep Learning, pages 1385-1405

Y. Chen, H. Yang, H. Pan, F. Siddiqui, A. Verdone, Q. Zhang, S. Chopra, C. Zhao, and Y. Shen. (2024), “BURExtract-Llama: An LLM for Clinical Concept Extraction in Breast Ultrasound Reports”, arXiv preprint arXiv:2408.11334

U. Sharma, J. Park, L. Heacock, S. Chopra, and K. Geras. (2024), “A training regime to learn unified representations from complementary breast imaging modalities”, arXiv preprint arXiv:2408.08560

W. Zhu, H. Tang, H. R. Rajamohan, D. Madaan, A. Chaudhari, S. Huang, X. Ma, S. Chopra, J. Dodson, A. A. Brody, A. V. Masurkar, and N. Razavian. (2024), “Predicting Alzheimer’s Diseases and Related Dementias in 3-year timeframe with AI Foundation Model on Electronic Health Records”, Alzheimer’s Association International Conference

C. Yen, R. Singhal, U. Sharma, R. Ranganath, S. Chopra, and L. Pinto. (2024), “Adaptive Sampling of k-Space in Magnetic Resonance for Rapid Pathology Prediction”, ICML

D. Madaan, T. Makino, S. Chopra, and K. Cho. (2024), “A Framework for Multi-modal Learning: Jointly Modeling Inter-& Intra-Modality Dependencies”, arXiv preprint arXiv:2405.17613

W. Zhu, H. Tang, H. Zhang, H. R. Rajamohan, S. Huang, X. Ma, A. Chaudhari, D. Madaan, E. Almahmoud, S. Chopra, J. A. Dodson, A. A. Brody, A. V. Masurkar, and N. Razavian. (2024), “Predicting Risk of Alzheimer’s Diseases and Related Dementias with AI Foundation Model on Electronic Health Records”, medRxiv

S. Kaur, S. Chopra, A. Nayyar, R. Sharma, and G. Singh. (2024), “A sequential convolutional neural network for image forgery detection”, Multimedia Tools and Applications, Vol. 83, pages 41311-41325

P. Liu, Y. Orru, C. Paxton, N. Shafiullah, and L. Pinto. (2024), “Ok-robot: What really matters in integrating open-knowledge models for robotics”, arXiv preprint arXiv:2401.12202

I. Guzey, Y. Dai, B. Evans, S. Chintala, and L. Pinto. (2024), “See to touch: Learning tactile dexterity through visual incentives”, 2024 IEEE International Conference on Robotics and Automation (ICRA), pages 13825-13832

S. Lee, Y. Wang, H. Etukuru, H. J. Kim, N. Shafiullah, and L. Pinto. (2024), “Behavior generation with latent actions”, ICML

R. Bhirangi, C. Wang, V. Pattabiraman, C. Majidi, A. Gupta, T. Hellebrekers, and L. Pinto. (2024), “Hierarchical state space models for continuous sequence-to-sequence modeling”, ICML

A. Iyer, Z. Peng, Y. Dai, I. Guzey, S. Haldar, S. Chintala, and L. Pinto. (2024), “Open teach: A versatile teleoperation system for robotic manipulation”, arXiv preprint arXiv:2403.07870

U. Piterbarg, L. Pinto, and R. Fergus. (2024), “Nethack is hard to hack”, Advances in Neural Information Processing Systems, Vol. 36

S. Haldar, Z. Peng, and L. Pinto. (2024), “BAKU: An Efficient Transformer for Multi-Task Policy Learning”, arXiv preprint arXiv:2406.07539

H. Etukuru, N. Naka, Z. Hu, S. Lee, J. Mehu, A. Edsinger, C. Paxton, S. Chintala, L. Pinto, and N. Shafiullah. (2024), “Robot Utility Models: General Policies for Zero-Shot Deployment in New Environments”, arXiv preprint arXiv:2409.05865

Z. J. Cui, H. Pan, A. Iyer, S. Haldar, and L. Pinto. (2024), “DynaMo: In-Domain Dynamics Pretraining for Visuo-Motor Control”, arXiv preprint arXiv:2409.12192

R. Bhirangi, V. Pattabiraman, E. Erciyes, Y. Cao, T. Hellebrekers, and L. Pinto. (2024), “AnySkin: Plug-and-play Skin Sensing for Robotic Touch”, arXiv preprint arXiv:2409.08276

C. Yen, R. Singhal, U. Sharma, R. Ranganath, S. Chopra, and L. Pinto. (2024), “Adaptive Sampling of k-Space in Magnetic Resonance for Rapid Pathology Prediction”, ICML

K. Huang, S. Arehalli, M. Kugemoto, C. Muxica, G. Prasad, B. Dillon, and T. Linzen. (2024), “Large-scale benchmark yields no evidence that language model surprisal explains syntactic disambiguation difficulty”, Journal of Memory and Language, Vol. 137, pages 104510

M. Mandelkern, and T. Linzen. (2024), “Do Language Models’ Words Refer?”, Computational Linguistics, pages 1-10

J. Petty, S. Steenkiste, I. Dasgupta, F. Sha, D. Garrette, and T. Linzen. (2024), “The Impact of Depth on Compositional Generalization in Transformer Language Models”, Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 7232-7245

S. Arehalli, and T. Linzen. (2024), “Neural networks as cognitive models of the processing of syntactic constraints”, Open Mind, Vol. 8, pages 558-614

W. Merrill, Z. Wu, N. Naka, Y. Kim, and T. Linzen. (2024), “Can You Learn Semantics Through Next-Word Prediction? The Case of Entailment”, ACL, pages 2752-2773

C. S. Leong, and T. Linzen. (2024), “Testing learning hypotheses using neural networks by manipulating learning data”, arXiv preprint arXiv:2407.04593

G. Prasad, and T. Linzen. (2024), “SPAWNing Structural Priming Predictions from a Cognitively Motivated Parser”, arXiv preprint arXiv:2403.07202

A. Warstadt, A. Mueller, L. Choshen, E. G. Wilcox, C. Zhuang, A. Williams, R. Cotterell, and T. Linzen. (2024), “Insights from the first BabyLM Challenge: Training sample-efficient language models on a developmentally plausible corpus”, Proceedings of the Annual Meeting of the Cognitive Science Society, Vol. 46

J. Petty, S. v. Steenkiste, and T. Linzen. (2024), “How Does Code Pretraining Affect Language Model Task Performance?”, arXiv preprint arXiv:2409.04556

C. S. Leong, and T. Linzen. (2024), “Testing learning hypotheses using neural networks by manipulating learning data”, arXiv e-prints, pages arXiv: 2407.04593

L. Choshen, R. Cotterell, M. Y. Hu, T. Linzen, A. Mueller, C. Ross, A. Warstadt, E. Wilcox, A. Williams, and C. Zhuang. (2024), “[Call for Papers] The 2nd BabyLM Challenge: Sample-efficient pretraining on a developmentally plausible corpus”, arXiv preprint arXiv:2404.06214

S. Ozaki, A. D. Santo, T. Linzen, and B. Dillon. (2024), “CCG parsing effort and surprisal jointly predict RT but underpredict garden-path effects”, Proceedings of the Society for Computation in Linguistics (SCiL), pages 362-364

T. H. Trinh, Y. Wu, Q. V. Le, H. He, and T. Luong. (2024), “Solving olympiad geometry without human demonstrations”, Nature, Vol. 625, pages 476-482

U. Anwar, A. Saparov, J. Rando, D. Paleka, M. Turpin, P. Hase, E. S. Lubana, E. Jenner, S. Casper, O. Sourbut, B. L. Edelman, Z. Zhang, M. Günther, A. Korinek, J. Hernandez-Orallo, L. Hammond, E. Bigelow, A. Pan, L. Langosco, T. Korbak, H. Zhang, R. Zhong, S. Ó. hÉigeartaigh, G. Recchia, G. Corsi, A. Chan, M. Anderljung, L. Edwards, Y. Bengio, D. Chen, S. Albanie, T. Maharaj, J. Foerster, F. Tramer, H. He, A. Kasirzadeh, Y. Choi, and D. Krueger. (2024), “Foundational challenges in assuring alignment and safety of large language models”, arXiv preprint arXiv:2404.09932

A. Saparov, R. Y. Pang, V. Padmakumar, N. Joshi, M. Kazemi, N. Kim, and H. He. (2024), “Testing the general deductive reasoning capacity of large language models using ood examples”, Advances in Neural Information Processing Systems, Vol. 36

R. Y. Pang, W. Yuan, K. Cho, H. He, S. Sukhbaatar, and J. Weston. (2024), “Iterative reasoning preference optimization”, arXiv preprint arXiv:2404.19733

H. R. Kirk, A. Whitefield, P. Röttger, A. Bean, K. Margatina, J. Ciro, R. Mosquera, M. Bartolo, A. Williams, H. He, B. Vidgen, and S. A. Hale. (2024), “The PRISM Alignment Project: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models”, arXiv preprint arXiv:2404.16019

Y. Chen, C. Zhao, Z. Yu, K. McKeown, and H. He. (2024), “Parallel structures in pre-training data yield in-context learning”, ACL, pages 8582-8592

Y. Chen, C. Singh, X. Liu, S. Zuo, B. Yu, H. He, and J. Gao. (2024), “Towards consistent natural-language explanations via explanation-consistency finetuning”, arXiv preprint arXiv:2401.13986

T. H. Trinh, Y. Wu, Q. V. Le, H. He, and T. Luong. (2024), “Author Correction: Solving olympiad geometry without human demonstrations”, Nature, Vol. 627, pages E8

J. Wen, R. Zhong, A. Khan, E. Perez, J. Steinhardt, M. Huang, S. R. Boman, H. He, and S. Feng. (2024), “Language Models Learn to Mislead Humans via RLHF”, arXiv preprint arXiv:2409.12822

J. Pan, H. He, S. R. Bowman, and S. Feng. (2024), “Spontaneous Reward Hacking in Iterative Self-Refinement”, arXiv preprint arXiv:2407.04549

N. Joshi, A. Saparov, Y. Wang, and H. He. (2024), “LLMs Are Prone to Fallacies in Causal Inference”, arXiv preprint arXiv:2406.12158

G. Wu, C. Zhao, C. Silva, and H. He. (2024), “Your Co-Workers Matter: Evaluating Collaborative Capabilities of Language Models in Blocks World”, ACL, pages 4941-4957

N. Gruver, M. Finzi, S. Qiu, and A. G. Wilson. (2024), “Large language models are zero-shot time series forecasters”, Advances in Neural Information Processing Systems, Vol. 36

N. Gruver, S. Stanton, N. Frey, T. G. Rudner, I. Hotzel, J. Lafrance-Vanasse, A. Rajpal, K. Cho, and A. G. Wilson. (2024), “Protein design with guided discrete diffusion”, Advances in neural information processing systems, Vol. 36

A. F. Ansari, L. Stella, C. Turkmen, X. Zhang, P. Mercado, H. Shen, O. Shchur, S. S. Rangapuram, S. P. Arango, S. Kapoor, J. Zschiegner, D. C. Maddix, M. W. Mahoney, K. Torkkola, A. G. Wilson, M. Bohlke-Schneider, and Y. Wang. (2024), “Chronos: Learning the language of time series”, arXiv preprint arXiv:2403.07815

M. Goldblum, H. Souri, R. Ni, M. Shu, V. Prabhu, G. Somepalli, P. Chattopadhyay, M. Ibrahim, A. Bardes, J. Hoffman, R. Chellappa, A. G. Wilson, and T. Goldstein. (2024), “Battle of the backbones: A large-scale comparison of pretrained models across computer vision tasks”, Advances in Neural Information Processing Systems, Vol. 36

N. Gruver, A. Sriram, A. Madotto, A. G. Wilson, C. L. Zitnick, and Z. Ulissi. (2024), “Fine-tuned language models generate stable inorganic materials as text”, ICLR

G. Detommaso, A. Gasparin, M. Donini, M. Seeger, A. G. Wilson, and C. Archambeau. (2024), “Fortuna: A library for uncertainty quantification in deep learning”, Journal of Machine Learning Research, Vol. 25, pages 1-7

T. Papamarkou, M. Skoularidou, K. Palla, L. Aitchison, J. Arbel, D. Dunson, M. Filippone, V. Fortuin, P. Hennig, A. Hubin, A. Immer, T. Karaletsos, M. E. Khan, A. Kristiadi, Y. Li, J. Lobato, S. Mandt, C. Nemeth, M. A. Osborne, T. G. Rudner, D. Rügamer, Y. W. Teh, M. Welling, A. G. Wilson, and R. Zhang. (2024), “Position paper: Bayesian deep learning in the age of large-scale ai”, ICML

V. Cherepanova, R. Levin, G. Somepalli, J. Geiping, C. B. Bruss, A. G. Wilson, T. Goldstein, and M. Goldblum. (2024), “A performance-driven benchmark for feature selection in tabular deep learning”, Advances in Neural Information Processing Systems, Vol. 36

A. Potapczynski, M. Finzi, G. Pleiss, and A. G. Wilson. (2024), “Cola: Exploiting compositional structure for automatic and efficient numerical linear algebra”, Advances in Neural Information Processing Systems, Vol. 36

T. Papamarkou, M. Skoularidou, K. Palla, L. Aitchison, J. Arbel, D. Dunson, M. Filippone, V. Fortuin, P. Hennig, J. M. Hernández-Lobato, A. Hubin, A. Immer, T. Karaletsos, M. E. Khan, A. Kristiadi, Y. Li, S. Mandt, C. Nemeth, M. A. Osborne, T. G. Rudner, D. Rügamer, Y. W. Teh, M. Welling, A. G. Wilson, and R. Zhang. (2024), “Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI”, Forty-first International Conference on Machine Learning

R. Shwartz-Ziv, M. Goldblum, Y. Li, C. B. Bruss, and A. G. Wilson. (2024), “Simplifying neural network training under class imbalance”, Advances in Neural Information Processing Systems, Vol. 36

S. Qiu, T. G. Rudner, S. Kapoor, and A. G. Wilson. (2024), “Should we learn most likely functions or parameters?”, Advances in Neural Information Processing Systems, Vol. 36

P. Kirichenko, M. Ibrahim, R. Balestriero, D. Bouchacourt, S. R. Vedantam, H. Firooz, and A. G. Wilson. (2024), “Understanding the detrimental class-level effects of data augmentation”, Advances in Neural Information Processing Systems, Vol. 36

S. Kapoor, N. Gruver, M. Roberts, K. Collins, A. Pal, U. Bhatt, A. Weller, S. Dooley, M. Goldblum, and A. G. Wilson. (2024), “Large Language Models Must Be Taught to Know What They Don’t Know”, arXiv preprint arXiv:2406.08391

T. G. Rudner, Y. S. Zhang, A. G. Wilson, and J. Kempe. (2024), “Mind the GAP: Improving Robustness to Subpopulation Shifts with Group-Aware Priors”, International Conference on Artificial Intelligence and Statistics, pages 127-135

S. Kapoor, N. Gruver, M. Roberts, A. Pal, S. Dooley, M. Goldblum, and A. Wilson. (2024), “Calibration-Tuning: Teaching Large Language Models to Know What They Don’t Know”, Proceedings of the 1st Workshop on Uncertainty-Aware NLP (UncertaiNLP 2024), pages 1-14

S. Qiu, A. Potapczynski, M. Finzi, M. Goldblum, and A. G. Wilson. (2024), “Compute Better Spent: Replacing Dense Layers with Structured Matrices”, ICML

H. Phan, A. G. Wilson, and Q. Lei. (2024), “Controllable Prompt Tuning For Balancing Group Distributional Robustness”, ICML

S. Lotfi, Y. Kuang, B. Amos, M. Goldblum, M. Finzi, and A. G. Wilson. (2024), “Unlocking Tokens as Data Points for Generalization Bounds on Larger Language Models”, arXiv preprint arXiv:2407.18158

T. Dharmawardena, C. Bailer-Jones, M. Fouesneau, D. Foreman-Mackey, P. Coronica, T. Colnaghi, T. Müller, and A. Wilson. (2024), “All-sky three-dimensional dust density and extinction Maps of the Milky Way out to 2.8 kpc”, Monthly Notices of the Royal Astronomical Society, pages stae1474

R. Shwartz-Ziv, M. Goldblum, A. Bansal, C. B. Bruss, Y. LeCun, and A. G. Wilson. (2024), “Just How Flexible are Neural Networks in Practice?”, arXiv preprint arXiv:2406.11463

A. N. Amin, and A. G. Wilson. (2024), “Scalable and Flexible Causal Discovery with an Efficient Test for Adjacency”, ICML

S. Qiu, B. Han, D. C. Maddix, S. Zhang, Y. Wang, and A. G. Wilson. (2024), “Transferring Knowledge from Large Foundation Models to Small Downstream Models”, ICML

A. N. Amin, and A. G. Wilson. (2024), “Scalable and Flexible Causal Discovery with an Efficient Test for Adjacency”, ICML

H. Souri, A. Bansal, H. Kazemi, L. Fowl, A. Saha, J. Geiping, A. G. Wilson, R. Chellappa, T. Goldstein, and M. Goldblum. (2024), “Generating Potent Poisons and Backdoors from Scratch with Guided Diffusion”, arXiv preprint arXiv:2403.16365

Y. LIU, K. Mallayya, M. Jovanovic, W. Maddox, A. Wilson, S. Klemenz, L. Schoop, and E. Kim. (2024), “Machine Learning Discovery of a New Descriptor for Topological Semimetal”, Bulletin of the American Physical Society

A. F. Ansari, L. Stella, C. Turkmen, X. Zhang, P. Mercado, H. Shen, O. Shchur, S. S. Rangapuram, S. P. Arango, S. Kapoor, J. Zschiegner, D. C. Maddix, M. W. Mahoney, K. Torkkola, A. G. Wilson, M. Bohlke-Schneider, and Y. Wang. (2024), “Chronos: Learning the Language of Time Series”, arXiv e-prints, pages arXiv: 2403.07815

Y. Hu, A. Lui, M. Goldstein, M. Sudarshan, A. Tinsay, C. Tsui, S. D. Maidman, J. Medamana, N. Jethani, A. Puli, V. Nguy, Y. Aphinyanaphongs, N. Kiefer, N. R. Smilowitz, J. Horowitz, T. Ahuja, G. I. Fishman, J. Hochman, S. Katz, S. Bernard, and R. Ranganath. (2024), “Development and external validation of a dynamic risk score for early prediction of cardiogenic shock in cardiac intensive care units using machine learning”, European Heart Journal: Acute Cardiovascular Care, pages zuae037

G. E. Moran, D. M. Blei, and R. Ranganath. (2024), “Holdout predictive checks for Bayesian model criticism”, Journal of the Royal Statistical Society Series B: Statistical Methodology, Vol. 86, pages 194-214

R. Singhal, M. Goldstein, and R. Ranganath. (2024), “What’s the score? Automated Denoising Score Matching for Nonlinear Diffusions”, ICML

A. Chen, S. Malladi, L. H. Zhang, X. Chen, Q. Zhang, R. Ranganath, and K. Cho. (2024), “Preference Learning Algorithms Do Not Learn Preference Rankings”, arXiv preprint arXiv:2405.19534

B. Yu, A. Kaku, K. Liu, A. Parnandi, E. Fokas, A. Venkatesan, N. Pandit, R. Ranganath, H. Schambra, and C. Fernandez-Granda. (2024), “Quantifying impairment and disease severity using AI models trained on healthy subjects”, npj Digital Medicine, Vol. 7, pages 180

H. Zhang, C. Tarabanis, N. Jethani, M. Goldstein, S. Smith, L. Chinitz, R. Ranganath, Y. Aphinyanaphongs, and L. Jankelson. (2024), “QTNet: Predicting Drug-Induced QT Prolongation With Artificial Intelligence–Enabled Electrocardiograms”, Clinical Electrophysiology, Vol. 10, pages 956-966

W. Amsterdam, P. Jong, J. J. Verhoeff, T. Leiner, and R. Ranganath. (2024), “From algorithms to action: improving patient care requires causality”, BMC medical informatics and decision making, Vol. 24, pages 111

L. H. Zhang, R. Ranganath, and A. Tafvizi. (2024), “Towards Minimal Targeted Updates of Language Models with Targeted Negative Training”, Trans. Mach. Learn. Res.

C. Yen, R. Singhal, U. Sharma, R. Ranganath, S. Chopra, and L. Pinto. (2024), “Adaptive Sampling of k-Space in Magnetic Resonance for Rapid Pathology Prediction”, ICML

A. Gandrakota, L. H. Zhang, A. Puli, K. Cranmer, J. Ngadiuba, R. Ranganath, and N. Tran. (2024), “Robust Anomaly Detection for Particle Physics Using Multi-Background Representation Learning”, Machine Learning: Science and Technology

W. K. Vong, W. Wang, A. E. Orhan, and B. M. Lake. (2024), “Grounded language acquisition through the eyes and ears of a single child”, Science, Vol. 383, pages 504-511

A. E. Orhan, and B. M. Lake. (2024), “Learning high-level visual representations from a child’s perspective without strong inductive biases”, Nature Machine Intelligence, Vol. 6, pages 271-283

Y. Zhou, R. Feinman, and B. M. Lake. (2024), “Compositional diversity in visual concept learning”, Cognition, Vol. 244, pages 105711

Y. Qin, W. Wang, and B. M. Lake. (2024), “A systematic investigation of learnability from single child linguistic input”, arXiv preprint arXiv:2402.07899

G. Davidson, G. Todd, J. Togelius, T. M. Gureckis, and B. M. Lake. (2024), “Goals as Reward-Producing Programs”, arXiv preprint arXiv:2405.13242

R. Teehan, B. Lake, and M. Ren. (2024), “CoLLEGe: Concept Embedding Generation for Large Language Models”, arXiv preprint arXiv:2403.15362

G. Davidson, A. E. Orhan, and B. M. Lake. (2024), “Spatial relation categorization in infants and deep neural networks”, Cognition, Vol. 245, pages 105690

A. E. Orhan, W. Wang, A. N. Wang, M. Ren, and B. M. Lake. (2024), “Self-supervised learning of video representations from a child’s perspective”, arXiv preprint arXiv:2402.00300

W. Li, S. C. Yasuda, M. R. Dillon, and B. Lake. (2024), “An Infant-Cognition Inspired Machine Benchmark for Identifying Agency, Affiliation, Belief, and Intention”, Proceedings of the Annual Meeting of the Cognitive Science Society, Vol. 46

S. LeGris, W. K. Vong, B. M. Lake, and T. M. Gureckis. (2024), “H-ARC: A Robust Estimate of Human Performance on the Abstraction and Reasoning Corpus Benchmark”, arXiv preprint arXiv:2409.01374

M. A. Lepori, A. R. Tartaglini, W. K. Vong, T. Serre, B. M. Lake, and E. Pavlick. (2024), “Beyond the Doors of Perception: Vision Transformers Represent Relations Between Objects”, arXiv preprint arXiv:2406.15955

Y. Zhou, B. M. Lake, and A. Williams. (2024), “Compositional learning of functions in humans and machines”, arXiv preprint arXiv:2403.12201

S. Kumar, R. Marjieh, B. Zhang, D. Campbell, M. Y. Hu, U. Bhatt, B. Lake, and T. L. Griffiths. (2024), “Comparing Abstraction in Humans and Large Language Models Using Multimodal Serial Reproduction”, arXiv preprint arXiv:2402.03618

C. S. Leong, and B. Lake. (2024), “Prompting invokes expert-like downward shifts in GPT-4V’s conceptual hierarchies”, Proceedings of the Annual Meeting of the Cognitive Science Society, Vol. 46

J. Spencer, B. Lake, R. Grieben, G. Schöner, M. Toneva, and G. Kuperberg. (2024), “Is Deep Learning the Answer for Understanding Human Cognitive Dynamics?”, Proceedings of the Annual Meeting of the Cognitive Science Society, Vol. 46

S. Kumar, R. Marjieh, B. Zhang, D. Campbell, M. Y. Hu, U. Bhatt, B. Lake, and T. Griffiths. (2024), “Comparing Abstraction in Humans and Machines Using Multimodal Serial Reproduction”, Proceedings of the Annual Meeting of the Cognitive Science Society, Vol. 46

K. Luo, B. Zhang, Y. Xiao, and B. Lake. (2024), “Finding Unsupervised Alignment of Conceptual Systems in Image-Word Representations”, Proceedings of the Annual Meeting of the Cognitive Science Society, Vol. 46

S. LeGris, B. Lake, and T. M. Gureckis. (2024), “Predicting Insight during Physical Reasoning”, Proceedings of the Annual Meeting of the Cognitive Science Society, Vol. 46

A. E. Orhan, W. Wang, A. N. Wang, M. Ren, and B. M. Lake. (2024), “Self-supervised learning of video representations from a child’s perspective”, arXiv e-prints, pages arXiv: 2402.00300

J. Bruna, B. Peherstorfer, and E. Vanden-Eijnden. (2024), “Neural Galerkin schemes with active learning for high-dimensional evolution equations”, Journal of Computational Physics, Vol. 496, pages 112588

D. Brandfonbrener, O. Nachum, and J. Bruna. (2024), “Inverse dynamics pretraining learns good representations for multitask imitation”, Advances in Neural Information Processing Systems, Vol. 36

A. Damian, L. Pillaud-Vivien, J. Lee, and J. Bruna. (2024), “Computational-Statistical Gaps in Gaussian Single-Index Models”, The Thirty Seventh Annual Conference on Learning Theory, pages 1262-1262

A. Zweig, L. Pillaud-Vivien, and J. Bruna. (2024), “On Single-Index Models beyond Gaussian Data”, Advances in Neural Information Processing Systems, Vol. 36

V. Kothapalli, T. Tirer, and J. Bruna. (2024), “A neural collapse perspective on feature evolution in graph neural networks”, Advances in Neural Information Processing Systems, Vol. 36

J. Bruna, and J. Han. (2024), “Posterior sampling with denoising oracles via tilted transport”, arXiv preprint arXiv:2407.00745

N. Amsel, G. Yehudai, and J. Bruna. (2024), “On the Benefits of Rank in Attention Layers”, arXiv preprint arXiv:2407.16153

L. Chen, J. Bruna, and A. Bietti. (2024), “How Truncating Weights Improves Reasoning in Language Models”, arXiv preprint arXiv:2406.03068

I. R. McKenzie, A. Lyzhov, M. Pieler, A. Parrish, A. Mueller, A. Prabhu, E. McLean, A. Kirtland, A. Ross, A. Liu, A. Gritsevskiy, D. Wurgaft, D. Kauffman, G. Recchia, J. Liu, J. Cavanagh, M. Weiss, S. Huang, T. F. Droid, T. Tseng, T. Korbak, X. Shen, Y. Zhang, Z. Zhou, N. Kim, S. R. Bowman, and E. Perez. (2024), “Inverse Scaling: When Bigger Isn’t Better”, TMLR

E. Hubinger, C. Denison, J. Mu, M. Lambert, M. Tong, M. MacDiarmid, T. Lanham, D. M. Ziegler, T. Maxwell, N. Cheng, A. Jermyn, A. Askell, A. Radhakrishnan, C. Anil, D. Duvenaud, D. Ganguli, F. Barez, J. Clark, K. Ndousse, K. Sachan, M. Sellitto, M. Sharma, N. DasSarma, R. Grosse, S. Kravec, Y. Bai, Z. Witten, M. Favaro, J. Brauner, H. Karnofsky, P. Christiano, S. R. Bowman, L. Graham, J. Kaplan, S. Mindermann, R. Greenblatt, B. Shlegeris, N. Schiefer, and E. Perez. (2024), “Sleeper agents: Training deceptive llms that persist through safety training”, arXiv preprint arXiv:2401.05566

A. Panickssery, S. R. Bowman, and S. Feng. (2024), “LLM evaluators recognize and favor their own generations”, arXiv preprint arXiv:2404.13076

C. Anil, E. Durmus, M. Sharma, J. Benton, S. Kundu, J. Batson, N. Rimsky, M. Tong, J. Mu, D. Ford, F. Mosconi, R. Agrawal, R. Schaeffer, N. Bashkansky, S. Svenningsen, M. Lambert, A. Radhakrishnan, C. Denison, E. J. Hubinger, Y. Bai, T. Bricken, T. Maxwell, N. Schiefer, J. Sully, A. Tamkin, T. Lanham, K. Nguyen, T. Korbak, J. Kaplan, D. Ganguli, S. R. Bowman, E. Perez, R. Grosse, and D. Duvenaud. (2024), “Many-shot jailbreaking”, Anthropic, April

A. Khan, J. Hughes, D. Valentine, L. Ruis, K. Sachan, A. Radhakrishnan, E. Grefenstette, S. R. Bowman, T. Rocktäschel, and E. Perez. (2024), “Debating with More Persuasive LLMs Leads to More Truthful Answers”, Proceedings of ICML

A. Chen, J. Phang, A. Parrish, V. Padmakumar, C. Zhao, S. R. Bowman, and K. Cho. (2024), “Two failures of self-consistency in the multi-step reasoning of llms”, TMLR

E. A. Hosseini, M. Schrimpf, Y. Zhang, S. Bowman, N. Zaslavsky, and E. Fedorenko. (2024), “Artificial neural network language models predict human brain responses to language even after a developmentally realistic amount of training”, Neurobiology of Language, Vol. 5, pages 43-63

J. Pfau, W. Merrill, and S. R. Bowman. (2024), “Let’s Think Dot by Dot: Hidden Computation in Transformer Language Models”, arXiv preprint arXiv:2404.15758

C. Denison, M. MacDiarmid, F. Barez, D. Duvenaud, S. Kravec, S. Marks, N. Schiefer, R. Soklaski, A. Tamkin, J. Kaplan, B. Shlegeris, S. R. Bowman, E. Perez, and E. Hubinger. (2024), “Sycophancy to Subterfuge: Investigating Reward-Tampering in Large Language Models”, arXiv preprint arXiv:2406.10162

J. Chua, E. Rees, H. Batra, S. R. Bowman, J. Michael, E. Perez, and M. Turpin. (2024), “Bias-Augmented Consistency Training Reduces Biased Reasoning in Chain-of-Thought”, arXiv preprint arXiv:2403.05518

A. C. Stickland, A. Lyzhov, J. Pfau, S. Mahdi, and S. R. Bowman. (2024), “Steering Without Side Effects: Improving Post-Deployment Control of Language Models”, arXiv e-prints, pages arXiv: 2406.15518

J. Pan, H. He, S. R. Bowman, and S. Feng. (2024), “Spontaneous Reward Hacking in Iterative Self-Refinement”, arXiv preprint arXiv:2407.04549

W. Yuan, R. Y. Pang, K. Cho, S. Sukhbaatar, J. Xu, and J. Weston. (2024), “Self-rewarding language models”, ICML

T. Hamamsy, J. T. Morton, R. Blackwell, D. Berenberg, N. Carriero, V. Gligorijevic, C. E. Strauss, J. K. Leman, K. Cho, and R. Bonneau. (2024), “Protein remote homology detection and structural alignment using deep learning”, Nature biotechnology, Vol. 42, pages 975-985

K. Martinkus, J. Ludwiczak, W. Liang, J. Lafrance-Vanasse, I. Hotzel, A. Rajpal, Y. Wu, K. Cho, R. Bonneau, V. Gligorijevic, and A. Loukas. (2024), “AbDiffuser: full-atom generation of in-vitro functioning antibodies”, Advances in Neural Information Processing Systems, Vol. 36

R. Y. Pang, W. Yuan, K. Cho, H. He, S. Sukhbaatar, and J. Weston. (2024), “Iterative reasoning preference optimization”, arXiv preprint arXiv:2404.19733

A. Chen, J. Scheurer, J. A. Campos, T. Korbak, J. S. Chan, S. R. Bowman, K. Cho, and E. Perez. (2024), “Learning from Natural Language Feedback”, Transactions on Machine Learning Research

W. Yuan, I. Kulikov, P. Yu, K. Cho, S. Sukhbaatar, J. Weston, and J. Xu. (2024), “Following length constraints in instructions”, arXiv preprint arXiv:2406.17744

A. Chen, S. Malladi, L. H. Zhang, X. Chen, Q. Zhang, R. Ranganath, and K. Cho. (2024), “Preference Learning Algorithms Do Not Learn Preference Rankings”, arXiv preprint arXiv:2405.19534

C. S. Gibbs, O. Mahmood, R. Bonneau, and K. Cho. (2024), “PMF-GRN: a variational inference approach to single-cell gene regulatory network inference using probabilistic matrix factorization”, Genome Biology, Vol. 25, pages 88

Y. Wang, and K. Cho. (2024), “Non-convolutional graph neural networks”, arXiv preprint arXiv:2408.00165

N. Tagasovska, J. W. Park, M. Kirchmeyer, N. C. Frey, A. M. Watkins, A. A. Ismail, A. R. Jamasb, E. Lee, T. Bryson, S. Ra, and K. Cho. (2024), “Antibody DomainBed: Out-of-Distribution Generalization in Therapeutic Protein Design”, arXiv preprint arXiv:2407.21028

L. Parker, F. Lanusse, S. Golkar, L. Sarra, M. Cranmer, A. Bietti, M. Eickenberg, G. Krawezik, M. McCabe, R. Morel, R. Ohana, M. Pettee, B. R. Blancard, K. Cho, S. Ho, and P. A. Collaboration. (2024), “AstroCLIP: a cross-modal foundation model for galaxies”, Monthly Notices of the Royal Astronomical Society, Vol. 531, pages 4990-5011

J. Kim, C. S. Gibbs, S. Yun, H. O. Song, and K. Cho. (2024), “Targeted Cause Discovery with Data-Driven Learning”, arXiv preprint arXiv:2408.16218

B. Su, J. Zhang, N. Collina, Y. Yan, D. Li, K. Cho, J. Fan, A. Roth, and W. J. Su. (2024), “Analysis of the ICML 2023 Ranking Data: Can Authors’ Opinions of Their Own Papers Assist Peer Review in Machine Learning?”, arXiv preprint arXiv:2408.13430

V. Sobal, M. Ibrahim, R. Balestriero, V. Cabannes, D. Bouchacourt, P. Astolfi, K. Cho, and Y. LeCun. (2024), “-Sample Contrastive Loss: Improving Contrastive Learning with Sample Similarity Graphs”, arXiv preprint arXiv:2407.18134

Y. Lee, S. Park, K. Cho, and J. Bak. (2024), “MentalAgora: A Gateway to Advanced Personalized Care in Mental Health through Multi-Agent Debating and Attribute Control”, arXiv preprint arXiv:2407.02736

F. Lanusse, L. Parker, S. Golkar, M. Cranmer, A. Bietti, M. Eickenberg, G. Krawezik, M. McCabe, R. Ohana, M. Pettee, B. R. Blancard, T. Tesileanu, K. Cho, S. Ho, and P. A. Collaboration. (2024), “AstroCLIP: Multimodal contrastive pretraining for astronomical data”, Astrophysics Source Code Library, pages ascl: 2407.015

S. Stanton, R. Alberstein, N. Frey, A. Watkins, and K. Cho. (2024), “Closed-Form Test Functions for Biophysical Sequence Optimization Algorithms”, arXiv preprint arXiv:2407.00236

D. Lee, H. O. Song, and K. Cho. (2024), “Training Greedy Policy for Proposal Batch Selection in Expensive Multi-Objective Combinatorial Optimization”, ICML

H. R. Rajamohan, R. Kijowski, K. Cho, and C. M. Deniz. (2024), “Modified Risk Formulation for Improving the Prediction of Knee Osteoarthritis Progression”, arXiv e-prints, pages arXiv: 2406.10119

S. Golkar, A. Bietti, M. Pettee, M. Eickenberg, M. Cranmer, K. Hirashima, G. Krawezik, N. Lourie, M. McCabe, R. Morel, R. Ohana, L. H. Parker, B. R. Blancard, K. Cho, and S. Ho. (2024), “Contextual Counting: A Mechanistic Study of Transformers on a Quantitative Task”, arXiv preprint arXiv:2406.02585

N. Tagasovska, V. Gligorijević, K. Cho, and A. Loukas. (2024), “Implicitly Guided Design with PropEn: Match your Data to Follow the Gradient”, arXiv preprint arXiv:2405.18075

D. Madaan, T. Makino, S. Chopra, and K. Cho. (2024), “A Framework for Multi-modal Learning: Jointly Modeling Inter-& Intra-Modality Dependencies”, arXiv preprint arXiv:2405.17613

K. Cho. (2024), “A Brief Introduction to Causal Inference in Machine Learning”, arXiv preprint arXiv:2405.08793

C. Zhang, S. Chen, O. Cigdem, H. R. Rajamohan, K. Cho, R. Kijowski, and C. M. Deniz. (2024), “MR-Transformer: Vision Transformer for Total Knee Replacement Prediction Using Magnetic Resonance Imaging”, arXiv preprint arXiv:2405.02784

O. Cigdem, S. Chen, C. Zhang, K. Cho, R. Kijowski, and C. M. Deniz. (2024), “Estimation of Time-to-Total Knee Replacement Surgery”, arXiv preprint arXiv:2405.00069

S. Bassi, D. Ataman, and K. Cho. (2024), “Generalization Measures for Zero-Shot Cross-Lingual Transfer”, arXiv preprint arXiv:2404.15928

H. R. Rajamohan, K. Cho, R. Kijowski, and C. Deniz. (2024), “DEEP LEARNING FOR PREDICTION OF KNEE OSTEOARTHRITIS STRUCTURAL PROGRESSION AND INCIDENCE”, Osteoarthritis and Cartilage, Vol. 32, pages S75-S76

S. Cha, and K. Cho. (2024), “Hyperparameters in Continual Learning: a Reality Check”, arXiv preprint arXiv:2403.09066

J. Tan, H. Le, J. Deng, Y. Liu, Y. Hao, M. Hollenberg, W. Liu, J. M. Wang, B. Xia, S. Ramaswami, V. Mezzano, C. Loomis, N. Murrell, A. L. Moreira, K. Cho, H. I. Pass, K. Wong, Y. Ban, B. G. Neel, A. Tsirigos, and D. Fenyo. (2024), “Characterization of tumor heterogeneity through segmentation-free representation learning”, bioRxiv, pages 2024.09. 05.611431

A. X. Lu, W. Yan, K. K. Yang, V. Gligorijevic, K. Cho, P. Abbeel, R. Bonneau, and N. Frey. (2024), “Tokenized and Continuous Embedding Compressions of Protein Sequence and Structure”, bioRxiv, pages 2024.08. 06.606920

B. Yan, and K. Cho. (2024), “CatScore: evaluating asymmetric catalyst design at high efficiency”, Digital Discovery, Vol. 3, pages 1624-1637

N. C. Frey, T. Joren, A. Ismail, A. Goodman, R. Bonneau, K. Cho, and V. Gligorijevic. (2024), “Cramming Protein Language Model Training in 24 GPU Hours”, bioRxiv, pages 2024.05. 14.594108

S. Tong, E. Brown, P. Wu, S. Woo, M. Middepogu, S. C. Akula, J. Yang, S. Yang, A. Iyer, X. Pan, A. Wang, R. Fergus, Y. LeCun, and S. Xie. (2024), “Cambrian-1: A fully open, vision-centric exploration of multimodal llms”, arXiv preprint arXiv:2406.16860

U. Piterbarg, L. Pinto, and R. Fergus. (2024), “Nethack is hard to hack”, Advances in Neural Information Processing Systems, Vol. 36

N. Monath, W. Grathwohl, M. Boratko, R. Fergus, A. McCallum, and M. Zaheer. (2024), “A Fresh Take on Stale Embeddings: Improving Dense Retriever Training with Corrector Networks”, ICML

A. GX-Chen, K. Marino, and R. Fergus. (2024), “Efficient Exploration and Discriminative World Model Learning with an Object-Centric Abstraction”, arXiv preprint arXiv:2408.11816

N. Yadav, N. Monath, M. Zaheer, R. Fergus, and A. McCallum. (2024), “Adaptive Retrieval and Scalable Indexing for k-NN Search with Cross-Encoders”, ICLR

S. Tong, Z. Liu, Y. Zhai, Y. Ma, Y. LeCun, and S. Xie. (2024), “Eyes wide shut? exploring the visual shortcomings of multimodal llms”, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9568-9578

X. He, Y. Tian, Y. Sun, N. V. Chawla, T. Laurent, Y. LeCun, X. Bresson, and B. Hooi. (2024), “G-retriever: Retrieval-augmented generation for textual graph understanding and question answering”, arXiv preprint arXiv:2402.07630

A. Bardes, Q. Garrido, J. Ponce, X. Chen, M. Rabbat, Y. LeCun, M. Assran, and N. Ballas. (2024), “Revisiting feature prediction for learning visual representations from video”, arXiv preprint arXiv:2404.08471

Y. Zhai, H. Bai, Z. Lin, J. Pan, S. Tong, Y. Zhou, A. Suhr, S. Xie, Y. LeCun, Y. Ma, and S. Levine. (2024), “Fine-Tuning Large Vision-Language Models as Decision-Making Agents via Reinforcement Learning”, arXiv preprint arXiv:2405.10292

R. Balestriero, and Y. LeCun. (2024), “Learning by reconstruction produces uninformative features for perception”, ICML

O. Press, R. Shwartz-Ziv, Y. LeCun, and M. Bethge. (2024), “The entropy enigma: Success and failure of entropy minimization”, ICML

Q. Garrido, M. Assran, N. Ballas, A. Bardes, L. Najman, and Y. LeCun. (2024), “Learning and leveraging world models in visual representation learning”, arXiv preprint arXiv:2403.00504

C. White, S. Dooley, M. Roberts, A. Pal, B. Feuer, S. Jain, R. Shwartz-Ziv, N. Jain, K. Saifullah, S. Naidu, C. Hegde, Y. LeCun, T. Goldstein, W. Neiswanger, and M. Goldblum. (2024), “Livebench: A challenging, contamination-free llm benchmark”, arXiv preprint arXiv:2406.19314

R. Balestriero, and Y. LeCun. (2024), “Fast and exact enumeration of deep networks partitions regions”, arXiv preprint arXiv:2401.11188

T. Moutakanni, P. Bojanowski, G. Chassagnon, C. Hudelot, A. Joulin, Y. LeCun, M. Muckley, M. Oquab, M. Revel, and M. Vakalopoulou. (2024), “Advancing human-centric AI for robust X-ray analysis through holistic self-supervised learning”, arXiv preprint arXiv:2405.01469

N. Hansen, J. SV, V. Sobal, Y. LeCun, X. Wang, and H. Su. (2024), “Hierarchical World Models as Visual Whole-Body Humanoid Controllers”, arXiv preprint arXiv:2405.18418

A. Bar, A. Bakhtiar, D. Tran, A. Loquercio, J. Rajasegaran, Y. LeCun, A. Globerson, and T. Darrell. (2024), “EgoPet: Egomotion and Interaction Data from an Animal’s Perspective”, arXiv preprint arXiv:2404.09991

A. N. Wang, C. Hoang, Y. Xiong, Y. LeCun, and M. Ren. (2024), “PooDLe: Pooled and dense self-supervised learning from naturalistic videos”, arXiv preprint arXiv:2408.11208

R. Shwartz-Ziv, M. Goldblum, A. Bansal, C. B. Bruss, Y. LeCun, and A. G. Wilson. (2024), “Just How Flexible are Neural Networks in Practice?”, arXiv preprint arXiv:2406.11463

R. Schaeffer, V. Lecomte, D. B. Pai, A. Carranza, B. Isik, A. Unell, M. Khona, T. Yerxa, Y. LeCun, S. Chung, A. Gromov, R. Shwartz-Ziv, and S. Koyejo. (2024), “Towards an Improved Understanding and Utilization of Maximum Manifold Capacity Representations”, arXiv preprint arXiv:2406.09366

L. Liu, T. Hospedales, Y. LeCun, M. Long, J. Luo, W. Ouyang, M. Pietikäinen, and T. Tuytelaars. (2024), “Learning With Fewer Labels in Computer Vision”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 46, pages 1319-1326

H. J. Jeon, J. D. Lee, Q. Lei, and B. V. Roy. (2024), “An Information-Theoretic Analysis of In-Context Learning”, ICML

Z. S. Zhong, X. Pan, and Q. Lei. (2024), “Bridging Domains with Approximately Shared Features”, arXiv preprint arXiv:2403.06424

H. Phan, A. G. Wilson, and Q. Lei. (2024), “Controllable Prompt Tuning For Balancing Group Distributional Robustness”, ICML

S. Liu, Z. Wang, and Q. Lei. (2024), “Data Reconstruction Attacks and Defenses: A Systematic Evaluation”, arXiv preprint arXiv:2402.09478

J. Li, Y. Dong, and Q. Lei. (2024), “Greedy Output Approximation: Towards Efficient Structured Pruning for LLMs Without Retraining”, arXiv preprint arXiv:2407.19126

Y. Dong, H. Phan, X. Pan, and Q. Lei. (2024), “Sketchy Moment Matching: Toward Fast and Provable Data Selection for Finetuning”, arXiv preprint arXiv:2407.06120

Q. Yu, Y. Wang, B. Huang, Q. Lei, and J. D. Lee. (2024), “Stochastic Zeroth-Order Optimization under Strongly Convexity and Lipschitz Hessian: Minimax Sample Complexity”, arXiv preprint arXiv:2406.19617

Z. Xue, Y. Dai, and Q. Lei. (2024), “Exploring Minimally Sufficient Representation in Active Learning through Label-Irrelevant Patch Augmentation”, Conference on Parsimony and Learning, pages 419-439

Z. He, J. Achterberg, K. Collins, K. Nejad, D. Akarca, Y. Yang, W. Gurnee, I. Sucholutsky, Y. Tang, R. Ianov, G. Ogden, C. Li, K. Sandbrink, S. Casper, A. Ivanova, and G. W. Lindsay. (2024), “Multilevel Interpretability Of Artificial Neural Networks: Leveraging Framework And Methods From Neuroscience”, arXiv preprint arXiv:2408.12664

O. Flasseur, T. Bodrito, J. Mairal, J. Ponce, M. Langlois, and A. Lagrange. (2024), “deep PACO: Combining statistical models with deep learning for exoplanet detection and characterization in direct imaging at high contrast”, Monthly Notices of the Royal Astronomical Society, Vol. 527, pages 1534–1562

N. Chahine, S. Ferradans, J. Vazquez-Corral, and J. Ponce. (2024), “Generalized portrait quality assessment”, arXiv preprint arXiv:2402.09178

N. Chahine, S. Ferradans, and J. Ponce. (2024), “PICNIQ: Pairwise Comparisons for Natural Image Quality Assessment”, arXiv preprint arXiv:2403.09746

O. Bounou, J. Ponce, and J. Carpentier. (2024), “Learning System Dynamics from Sensory Input under Optimal Control Principles”, Proc. Conference on Decision and Control (CDC 2024)

Y. d. Mont-Marin, M. Hebert, and J. Ponce. (2024), “Geodesic turnpikes for robot motion planning”, Proc. WAFR

E. Vincent, M. Saroufim, J. Chemla, Y. Ubelmann, P. Marquis, J. Ponce, and M. Aubry. (2024), “Detecting Looted Archaeological Sites from Satellite Image Time Series”, arXiv preprint arXiv:2409.09432

T. Bodrito, O. Flasseur, J. Mairal, J. Ponce, M. Langlois, and A. Lagrange. (2024), “MODEL&CO: Exoplanet detection in angular differential imaging by learning across multiple observations”, Monthly Notices of the Royal Astronomical Society (Accepted for publication)

E. Vincent, J. Ponce, and M. Aubry. (2024), “Satellite Image Time Series Semantic Change Detection: Novel Architecture and Analysis of Domain Shift”, arXiv preprint arXiv:2407.07616

S. Tong, Z. Liu, Y. Zhai, Y. Ma, Y. LeCun, and S. Xie. (2024), “Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs”, CVPR 2024

P. Wu, and S. Xie. (2024), “V*: Guided Visual Search as a Core Mechanism in Multimodal LLMs”, CVPR 2024

N. Ma, M. Goldstein, M. S. Albergo, N. M. Boffi, E. Vanden-Eijnden, and S. Xie. (2024), “SIT: Exploring flow and diffusion-based generative models with scalable interpolant transformers”, ECCV 2024

S. Tong, E. Brown, P. Wu, S. Woo, M. Middepogu, S. C. Akula, J. Yang, S. Yang, A. Iyer, X. Pan, A. Wang, R. Fergus, Y. LeCun, and S. Xie. (2024), “Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs”, arXiv preprint arXiv:2406.16860

X. Chen, Z. Liu, S. Xie, and K. He. (2024), “Deconstructing denoising diffusion models for self-supervised learning”, arXiv preprint arXiv:2401.14404

J. Yang, R. Ding, E. Brown, X. Qi, and S. Xie. (2024), “V-IRL: Grounding virtual intelligence in real life”, ECCV 2024

J. Yenphraphai, X. Pan, S. Liu, D. Panozzo, and S. Xie. (2024), “Image sculpting: Precise object editing with 3d geometry control”, CVPR 2024

J. Ma, P. Huang, S. Xie, S. Li, L. Zettlemoyer, S. Chang, W. Yih, and H. Xu. (2024), “MoDE: CLIP Data Experts via Clustering”, CVPR 2024

H. Zhao, H. Weng, D. Lu, A. Li, J. Li, A. Panda, and S. Xie. (2024), “On Scaling Up 3D Gaussian Splatting Training”, arXiv preprint arXiv:2406.18533

M. Tao, and S. Xie. (2024), “What Does a Visual Formal Analysis of the World’s 500 Most Famous Paintings Tell Us About Multimodal LLMs?”, ICLR 2024

T. Yerxa, Y. Kuang, E. Simoncelli, and S. Chung. (2024), “Learning efficient coding of natural images with maximum manifold capacity representations”, Advances in Neural Information Processing Systems, Vol. 36

A. Canatar, J. Feather, A. Wakhloo, and S. Chung. (2024), “A spectral theory of neural prediction and alignment”, Advances in Neural Information Processing Systems, Vol. 36

M. Kuoch*, C. Chou*, N. Parthasarathy, J. Dapello, J. J. DiCarlo, H. Sompolinsky, and S. Chung. (2024), “Probing Biological and Artificial Neural Networks with Task-dependent Neural Manifolds”, Conference on Parsimony and Learning (Proceedings Track)

A. J. Wakhloo, W. Slatton, and S. Chung. (2024), “Neural Population Geometry and Optimal Coding of Tasks with Shared Latent Structure”, arXiv preprint arXiv:2402.16770

C. Chou, L. Arend, A. J. Wakhloo, R. Kim, W. Slatton, and S. Chung. (2024), “Neural Manifold Capacity Captures Representation Geometry, Correlations, and Task-Efficiency Across Species and Behaviors”, bioRxiv, pages 2024.02. 26.582157

F. Mignacco, C. Chou, and S. Chung. (2024), “Nonlinear classification of neural manifolds with contextual information”, arXiv preprint arXiv:2405.06851

G. Tuckute, D. Finzi, E. Margalit, J. Zylberberg, S. Chung, A. Fyshe, E. Fedorenko, N. Kriegeskorte, J. Yates, K. G. Spector, and K. Kar. (2024), “How to optimize neuroscience data utilization and experiment design for advancing primate visual and linguistic brain models?”, arXiv preprint arXiv:2401.03376

J. Yang, X. Chen, S. Qian, N. Madaan, M. Iyengar, D. F. Fouhey, and J. Chai. (2024), “Llm-grounder: Open-vocabulary 3d visual grounding with large language model as an agent”, 2024 IEEE International Conference on Robotics and Automation (ICRA), pages 7694-7701

G. Pavlakos, D. Shan, I. Radosavovic, A. Kanazawa, D. Fouhey, and J. Malik. (2024), “Reconstructing hands in 3d with transformers”, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9826-9836

N. Kulkarni, D. Rempe, K. Genova, A. Kundu, J. Johnson, D. Fouhey, and L. Guibas. (2024), “Nifty: Neural object interaction fields for guided human motion synthesis”, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 947-957

V. Tschernezki, A. Darkhalil, Z. Zhu, D. Fouhey, I. Laina, D. Larlus, D. Damen, and A. Vedaldi. (2024), “Epic fields: Marrying 3d geometry and video understanding”, Advances in Neural Information Processing Systems, Vol. 36

X. Chen, Z. Ma, X. Zhang, S. Xu, S. Qian, J. Yang, D. F. Fouhey, and J. Chai. (2024), “Multi-Object Hallucination in Vision-Language Models”, arXiv preprint arXiv:2407.06192

C. Rockwell, N. Kulkarni, L. Jin, J. J. Park, J. Johnson, and D. F. Fouhey. (2024), “FAR: Flexible Accurate and Robust 6DoF Relative Camera Pose Estimation”, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19854-19864

S. Qian, K. Mo, V. Blukis, D. F. Fouhey, D. Fox, and A. Goyal. (2024), “3D-MVP: 3D Multiview Pretraining for Robotic Manipulation”, arXiv preprint arXiv:2406.18158

L. Jin, N. Kulkarni, and D. F. Fouhey. (2024), “3DFIRES: Few Image 3D REconstruction for Scenes with Hidden Surfaces”, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9742-9751

R. Wang, D. F. Fouhey, R. E. Higgins, S. K. Antiochos, G. Barnes, J. T. Hoeksema, K. Leka, Y. Liu, P. W. Schuck, and T. I. Gombosi. (2024), “SuperSynthIA: Physics-ready Full-disk Vector Magnetograms from HMI, Hinode, and Machine Learning”, The Astrophysical Journal, Vol. 970, pages 168

S. Jabbour, G. Kondas, E. Kazerooni, M. Sjoding, D. Fouhey, and J. Wiens. (2024), “DEPICT: Diffusion-Enabled Permutation Importance for Image Classification Tasks”, arXiv preprint arXiv:2407.14509

J. Yang, X. Chen, N. Madaan, M. Iyengar, S. Qian, D. F. Fouhey, and J. Chai. (2024), “3D-GRAND: A Million-Scale Dataset for 3D-LLMs with Better Grounding and Less Hallucination”, arXiv preprint arXiv:2406.05132

J. Yang, X. Chen, N. Madaan, M. Iyengar, S. Qian, D. F. Fouhey, and J. Chai. (2024), “3D-GRAND: Towards Better Grounding and Less Hallucination for 3D-LLMs”, arXiv e-prints, pages arXiv: 2406.05132

2023

L. Zhang, A. J. Yang, Y. Xiong, S. Casas, B. Yang, M. Ren, and R. Urtasun. (2023), “Towards Unsupervised Object Detection From LiDAR Point Clouds”, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9317-9328

D. Mayo, T. R. Scott, M. Ren, G. Elsayed, K. Hermann, M. Jones, and M. Mozer. (2023), “Multitask Learning Via Interleaving: A Neural Network Investigation”, Proceedings of the Annual Meeting of the Cognitive Science Society, Vol. 45

R. Tibrewala, T. Dutt, A. Tong, L. Ginocchio, M. B. Keerthivasan, S. H. Baete, S. Chopra, Y. W. Lui, D. K. Sodickson, H. Chandarana, and P. M. Johnson. (2023), “FastMRI Prostate: A Publicly Available, Biparametric MRI Dataset to Advance Machine Learning for Prostate Cancer Imaging”, arXiv preprint arXiv:2304.09254

D. Madaan, D. Sodickson, K. Cho, and S. Chopra. (2023), “On Sensitivity and Robustness of Normalization Schemes to Input Distribution Shifts in Automatic MR Image Diagnosis”, arXiv preprint arXiv:2306.13276

H. Huang, S. Rawlekar, S. Chopra, and C. M. Deniz. (2023), “Radiology Reports Improve Visual Representations Learned from Radiographs”, Medical Imaging with Deep Learning

R. Singhal, M. Sudarshan, A. Mahishi, S. Kaushik, L. Ginocchio, A. Tong, H. Chandarana, D. K. Sodickson, R. Ranganath, and S. Chopra. (2023), “On the Feasibility of Machine Learning Augmented Magnetic Resonance for Point-of-Care Identification of Disease”, arXiv preprint arXiv:2301.11962

S. P. Arunachalam, S. Silwal, B. Evans, and L. Pinto. (2023), “Dexterous imitation made easy: A learning-based framework for efficient dexterous manipulation”, 2023 ieee international conference on robotics and automation (icra), pages 5954-5961

S. Haldar, V. Mathur, D. Yarats, and L. Pinto. (2023), “Watch and match: Supercharging imitation with regularized optimal transport”, Conference on Robot Learning, pages 32-43

S. P. Arunachalam, I. Güzey, S. Chintala, and L. Pinto. (2023), “Holo-dex: Teaching dexterity with immersive mixed reality”, 2023 IEEE International Conference on Robotics and Automation (ICRA), pages 5962-5969

A. Padalkar, A. Pooley, A. Jain, A. Bewley, A. Herzog, A. Irpan, A. Khazatsky, A. Rai, A. Singh, A. Brohan, A. Raffin, A. Wahid, B. Burgess-Limerick, B. Kim, B. Schölkopf, B. Ichter, C. Lu, C. Xu, C. Finn, C. Xu, C. Chi, C. Huang, C. Chan, C. Pan, C. Fu, C. Devin, D. Driess, D. Pathak, D. Shah, D. Büchler, D. Kalashnikov, D. Sadigh, E. Johns, F. Ceola, F. Xia, F. Stulp, G. Zhou, G. S. Sukhatme, G. Salhotra, G. Yan, G. Schiavi, G. Kahn, H. Su, H. Fang, H. Shi, H. B. Amor, H. I. Christensen, H. Furuta, H. Walke, H. Fang, I. Mordatch, I. Radosavovic, I. Leal, J. Liang, J. Abou-Chakra, J. Kim, J. Peters, J. Schneider, J. Hsu, J. Bohg, J. Bingham, J. Wu, J. Wu, J. Luo, J. Gu, J. Tan, J. Oh, J. Malik, J. Tompson, J. Yang, J. J. Lim, J. Silvério, J. Han, K. Rao, K. Pertsch, K. Hausman, K. Go, K. Gopalakrishnan, K. Goldberg, K. Byrne, K. Oslund, K. Kawaharazuka, K. Zhang, K. Rana, K. Srinivasan, L. Y. Chen, L. Pinto, L. Tan, L. Ott, L. Lee, M. Tomizuka, M. Du, M. Ahn, M. Zhang, M. Ding, M. K. Srirama, M. Sharma, M. J. Kim, N. Kanazawa, N. Hansen, N. Heess, N. J. Joshi, N. Suenderhauf, N. D. Palo, N. Shafiullah, O. Mees, O. Kroemer, P. R. Sanketi, P. Wohlhart, P. Xu, P. Sermanet, P. Sundaresan, Q. Vuong, R. Rafailov, R. Tian, R. Doshi, R. Martín-Martín, R. Mendonca, R. Shah, R. Hoque, R. Julian, S. Bustamante, S. Kirmani, S. Levine, S. Moore, S. Bahl, S. Dass, S. Sonawani, S. Song, S. Xu, S. Haldar, S. Adebola, S. Guist, S. Nasiriany, S. Schaal, S. Welker, S. Tian, S. Dasari, S. Belkhale, T. Osa, T. Harada, T. Matsushima, T. Xiao, T. Yu, T. Ding, T. Davchev, T. Z. Zhao, T. Armstrong, T. Darrell, and V. Jain. (2023), “Open X-Embodiment: Robotic learning datasets and RT-X models”, arXiv preprint arXiv:2310.08864

I. Guzey, B. Evans, S. Chintala, and L. Pinto. (2023), “Dexterity from Touch: Self-Supervised Pre-Training of Tactile Representations with Robotic Play”, arXiv preprint arXiv:2303.12076

S. Haldar, J. Pari, A. Rai, and L. Pinto. (2023), “Teach a Robot to FISH: Versatile Imitation from One Minute of Demonstrations”, Robotics – Science and Systems

I. Guzey, Y. Dai, B. Evans, S. Chintala, and L. Pinto. (2023), “See to touch: Learning tactile dexterity through visual incentives”, arXiv preprint arXiv:2309.12300

U. Piterbarg, L. Pinto, and R. Fergus. (2023), “NetHack is Hard to Hack”, arXiv preprint arXiv:2305.19240

G. Zhou, V. Dean, M. K. Srirama, A. Rajeswaran, J. Pari, K. Hatch, A. Jain, T. Yu, P. Abbeel, L. Pinto, C. Finn, and A. Gupta. (2023), “Train Offline, Test Online: A Real Robot Learning Benchmark”, ICRA, pages 9197-9203

Q. Vuong, S. Levine, H. R. Walke, K. Pertsch, A. Singh, R. Doshi, C. Xu, J. Luo, L. Tan, D. Shah, C. Finn, M. Du, M. J. Kim, A. Khazatsky, J. H. Yang, T. Z. Zhao, K. Goldberg, R. Hoque, L. Y. Chen, S. Adebola, G. S. Sukhatme, G. Salhotra, S. Dass, L. Pinto, Z. J. Cui, S. Haldar, A. Rai, N. Shafiullah, Y. Zhu, Y. Zhu, S. Nasiriany, S. Song, C. Chi, C. Pan, W. Burgard, O. Mees, C. Huang, D. Pathak, S. Bahl, R. Mendonca, G. Zhou, M. K. Srirama, S. Dasari, C. Lu, H. Fang, H. Fang, H. I. Christensen, M. Tomizuka, W. Zhan, M. Ding, C. Xu, X. Zhu, R. Tian, Y. Lee, D. Sadigh, Y. Cui, S. Belkhale, P. Sundaresan, T. Darrell, J. Malik, I. Radosavovic, J. Bohg, K. Srinivasan, X. Wang, N. Hansen, Y. Wu, G. Yan, H. Su, J. Gu, X. Li, N. Suenderhauf, K. Rana, B. Burgess-Limerick, F. Ceola, K. Kawaharazuka, N. Kanazawa, T. Matsushima, Y. Matsuo, Y. Iwasawa, H. Furuta, J. Oh, T. Harada, T. Osa, Y. Tang, O. Kroemer, M. Sharma, K. L. Zhang, B. Kim, Y. Cho, J. Han, J. Kim, J. J. Lim, E. Johns, N. D. Palo, F. Stulp, A. Raffin, S. Bustamante, J. Silvério, A. Padalkar, J. Peters, B. Schölkopf, D. Büchler, J. Schneider, S. Guist, J. Wu, S. Tian, H. Shi, Y. Li, Y. Wang, M. Zhang, H. B. Amor, Y. Zhou, K. Majd, L. Ott, G. Schiavi, R. Martín-Martín, R. Shah, Y. Bisk, J. T. Bingham, T. Yu, V. Jain, T. Xiao, K. Hausman, C. Chan, A. Herzog, Z. Xu, S. Kirmani, V. Vanhoucke, R. Julian, L. Lee, T. Ding, Y. Chebotar, J. Tan, J. Liang, I. Mordatch, K. Rao, Y. Lu, K. Gopalakrishnan, S. Welker, N. J. Joshi, C. M. Devin, A. Irpan, S. Moore, A. Wahid, J. Wu, X. Chen, P. Wohlhart, A. Bewley, W. Zhou, and I. Leal. (2023), “Open X-Embodiment: Robotic Learning Datasets and RT-X Models”, Towards Generalist Robots: Learning Paradigms for Scalable Skill Acquisition@ CoRL2023

S. Haldar, and L. Pinto. (2023), “PolyTask: Learning Unified Policies through Behavior Distillation”, arXiv preprint arXiv:2310.08573

J. Hejna, P. Abbeel, and L. Pinto. (2023), “Improving long-horizon imitation through instruction prediction”, Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 37, pages 7857-7865

R. T. McCoy, P. Smolensky, T. Linzen, J. Gao, and A. Celikyilmaz. (2023), “How much do language models copy from their training data? evaluating linguistic novelty in text generation using raven”, Transactions of the Association for Computational Linguistics, Vol. 11, pages 652-670

A. Yedetore, T. Linzen, R. Frank, and R. T. McCoy. (2023), “How poor is the stimulus? Evaluating hierarchical generalization in neural networks trained on child-directed speech”, ACL, pages 9370-9393

A. Mueller, and T. Linzen. (2023), “How to Plant Trees in Language Models: Data and Architectural Effects on the Emergence of Syntactic Inductive Biases”, ACL, pages 11237-11252

A. Kobzeva, S. Arehalli, T. Linzen, and D. Kush. (2023), “Neural Networks Can Learn Patterns of Island-insensitivity in Norwegian”, Proceedings of the Society for Computation in Linguistics, Vol. 6, pages 175-185

T. Eisape, M. Tessler, I. Dasgupta, F. Sha, S. v. Steenkiste, and T. Linzen. (2023), “A Systematic Comparison of Syllogistic Reasoning in Humans and Language Models”, arXiv preprint arXiv:2311.00445

J. Petty, S. v. Steenkiste, I. Dasgupta, F. Sha, D. Garrette, and T. Linzen. (2023), “The Impact of Depth and Width on Transformer Language Model Generalization”, arXiv preprint arXiv:2310.19956

W. Timkey, and T. Linzen. (2023), “A Language Model with Limited Memory Capacity Captures Interference in Human Sentence Processing”, arXiv preprint arXiv:2310.16142

S. Hao, and T. Linzen. (2023), “Verb Conjugation in Transformers Is Determined by Linear Encodings of Subject Number”, arXiv preprint arXiv:2310.15151

B. Li, L. Donatelli, A. Koller, T. Linzen, Y. Yao, and N. Kim. (2023), “SLOG: A Structural Generalization Benchmark for Semantic Parsing”, arXiv preprint arXiv:2310.15040

M. Mandelkern, and T. Linzen. (2023), “Do Language Models Refer?”, EMNLP, pages 977-982

C. S. Leong, and T. Linzen. (2023), “Language Models Can Learn Exceptions to Syntactic Rules”, arXiv preprint arXiv:2306.05969

A. Mueller, and T. Linzen. (2023), “How to Plant Trees in LMs: Data and Architectural Effects on the Emergence of Syntactic Inductive Biases”, Proceedings of the conference Association for Computational Linguistics Meeting

A. Saparov, R. Y. Pang, V. Padmakumar, N. Joshi, S. M. Kazemi, N. Kim, and H. He. (2023), “Testing the General Deductive Reasoning Capacity of Large Language Models Using OOD Examples”, arXiv preprint arXiv:2305.15269

C. Si, D. Friedman, N. Joshi, S. Feng, D. Chen, and H. He. (2023), “Measuring Inductive Biases of In-Context Learning with Underspecified Demonstrations”, ACL, pages 11289-11310

V. Padmakumar, and H. He. (2023), “Does Writing with Language Models Reduce Content Diversity?”, arXiv preprint arXiv:2309.05196

Y. Chen, R. Zhong, N. Ri, C. Zhao, H. He, J. Steinhardt, Z. Yu, and K. McKeown. (2023), “Do models explain themselves? counterfactual simulatability of natural language explanations”, arXiv preprint arXiv:2307.08678

A. C. Stickland, S. Sengupta, J. Krone, S. Mansour, and H. He. (2023), “Robustification of Multilingual Language Models to Real-world Noise in Crosslingual Zero-shot Settings with Robust Contrastive Pretraining”, Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 1367-1383

V. Padmakumar, R. Y. Pang, H. He, and A. P. Parikh. (2023), “Extrapolative Controlled Sequence Generation via Iterative Refinement”, ICML, pages 26792-26808

R. Y. Pang, S. Roller, K. Cho, H. He, and J. Weston. (2023), “Leveraging Implicit Feedback from Deployment Data in Dialogue”, arXiv preprint arXiv:2307.14117

S. Venkatraman, H. He, and D. Reitter. (2023), “How do decoding algorithms distribute information in dialogue responses?”, EACL, pages 923-932

N. Joishi, J. Rando, A. Saparov, N. Kim, and H. He. (2023), “Personas as a Way to Model Truthfulness in Language Models”, arXiv preprint arXiv:2310.18168

R. Y. Pang, S. Roller, K. Cho, H. He, and J. Weston. (2023), “Leveraging Implicit Feedback from Deployment Data in Dialogue”, arXiv e-prints, pages arXiv: 2307.14117

C. Yang, F. Yin, H. He, K. Chang, X. Ma, and B. Xiang. (2023), “Efficient Shapley Values Estimation by Amortization for Text Classification”, ACL, pages 8666-8680

C. Peyser, M. Picheny, K. Cho, R. Prabhavalkar, W. R. Huang, and T. N. Sainath. (2023), “A Comparison of Semi-Supervised Learning Techniques for Streaming ASR at Scale”, ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1-5

C. Peyser, R. Huang, T. Sainath, R. Prabhavalkar, M. Picheny, and K. Cho. (2023), “Dual Learning for Large Vocabulary On-Device ASR”, 2022 IEEE Spoken Language Technology Workshop (SLT), pages 245-251

C. Peyser, Z. Meng, K. Hu, R. Prabhavalkar, A. Rosenberg, T. N. Sainath, M. Picheny, and K. Cho. (2023), “Improving Joint Speech-Text Representations Without Alignment”, arXiv preprint arXiv:2308.06125

D. Yu, Y. Gong, M. A. Picheny, B. Ramabhadran, D. Hakkani-Tür, R. Prasad, H. Zen, J. Skoglund, J. H. Černocký, L. Burget, and A. Mohamed. (2023), “Twenty-Five Years of Evolution in Speech and Language Processing”, IEEE Signal Processing Magazine, Vol. 40, pages 27-39

R. Balestriero, M. Ibrahim, V. Sobal, A. Morcos, S. Shekhar, T. Goldstein, F. Bordes, A. Bardes, G. Mialon, Y. Tian, A. Schwarzschild, A. G. Wilson, J. Geiping, Q. Garrido, P. Fernandez, A. Bar, H. Pirsiavash, Y. LeCun, and M. Goldblum. (2023), “A cookbook of self-supervised learning”, arXiv preprint arXiv:2304.12210

N. Gruver, S. Stanton, N. C. Frey, T. G. Rudner, I. Hotzel, J. Lafrance-Vanasse, A. Rajpal, K. Cho, and A. G. Wilson. (2023), “Protein Design with Guided Discrete Diffusion”, arXiv preprint arXiv:2305.20009

G. Detommaso, A. Gasparin, M. Donini, M. Seeger, A. G. Wilson, and C. Archambeau. (2023), “Fortuna: A Library for Uncertainty Quantification in Deep Learning”, arXiv preprint arXiv:2302.04019

N. Gruver, M. Finzi, S. Qiu, and A. G. Wilson. (2023), “Large language models are zero-shot time series forecasters”, arXiv preprint arXiv:2310.07820

S. Qiu, A. Potapczynski, P. Izmailov, and A. G. Wilson. (2023), “Simple and Fast Group Robustness by Automatic Feature Reweighting”, ICML, pages 28448-28467

M. Finzi, A. Potapczynski, M. Choptuik, and A. G. Wilson. (2023), “A Stable and Scalable Method for Solving Initial Value PDEs with Neural Networks”, ICLR

S. Stanton, W. Maddox, and A. G. Wilson. (2023), “Bayesian Optimization with Conformal Prediction Sets”, International Conference on Artificial Intelligence and Statistics, pages 959-986

Y. L. Li, T. G. Rudner, and A. G. Wilson. (2023), “A Study of Bayesian Neural Network Surrogates for Bayesian Optimization”, arXiv preprint arXiv:2305.20028

R. Aly, X. Shi, K. Lin, A. Zhang, and A. G. Wilson. (2023), “Automated Few-shot Classification with Instruction-Finetuned Language Models”, arXiv preprint arXiv:2305.12576

M. Goldblum, M. Finzi, K. Rowan, and A. G. Wilson. (2023), “The No Free Lunch Theorem, Kolmogorov Complexity, and the Role of Inductive Biases in Machine Learning”, arXiv preprint arXiv:2304.05366

I. Bhattacharya, B. A. Johnson, W. J. Artman, A. Wilson, K. G. Lynch, J. R. McKay, and A. Ertefaie. (2023), “A non‐parametric Bayesian approach for adjusting partial compliance in sequential decision making”, Statistics in Medicine

S. Qiu, T. G. Rudner, S. Kapoor, and A. G. Wilson. (2023), “Should We Learn Most Likely Functions or Parameters?”, Thirty-seventh Conference on Neural Information Processing Systems

Y. Wang, T. G. Rudner, and A. G. Wilson. (2023), “Visual Explanations of Image-Text Representations via Multi-Modal Information Bottleneck Attribution”, Thirty-seventh Conference on Neural Information Processing Systems

M. Goldblum, H. Souri, R. Ni, M. Shu, V. Prabhu, G. Somepalli, P. Chattopadhyay, M. Ibrahim, A. Bardes, J. Hoffman, R. Chellappa, A. G. Wilson, and T. Goldstein. (2023), “Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks”, arXiv preprint arXiv:2310.19909

V. Cherepanova, R. Levin, G. Somepalli, J. Geiping, C. Bruss, A. Wilson, T. Goldstein, and M. Goldblum. (2023), “A Performance-Driven Benchmark for Feature Selection in Tabular Deep Learning”, NeurIPS 2023 Second Table Representation Learning Workshop

A. Potapczynski, M. Finzi, G. Pleiss, and A. G. Wilson. (2023), “CoLA: Exploiting Compositional Structure for Automatic and Efficient Numerical Linear Algebra”, arXiv preprint arXiv:2309.03060

M. A. Finzi, A. Boral, A. G. Wilson, F. Sha, and L. Zepeda-Núñez. (2023), “User-defined event sampling and uncertainty quantification in diffusion models for physical dynamical systems”, International Conference on Machine Learning, pages 10136-10152

B. D. Williamson, R. Wyss, E. A. Stuart, L. E. Dang, A. N. Mertens, A. Wilson, and S. Gruber. (2023), “An Application of the Causal Roadmap in Two Safety Monitoring Case Studies: Covariate-Adjustment and Outcome Prediction using Electronic Health Record Data”, arXiv preprint arXiv:2305.07564

P. Kirichenko, R. Balestriero, M. Ibrahim, S. R. Vedantam, H. Firooz, and A. G. Wilson. (2023), “Understanding the class-specific effects of data augmentations”, ICLR 2023 Workshop on Pitfalls of limited data and computation for Trustworthy ML

B. D. Williamson, R. Wyss, E. A. Stuart, L. E. Dang, A. N. Mertens, R. S. Neugebauer, A. Wilson, and S. Gruber. (2023), “An application of the Causal Roadmap in two safety monitoring case studies: Causal inference and outcome prediction using electronic health record data”, Journal of Clinical and Translational Science, Vol. 7, pages e208

R. Singhal, M. Goldstein, and R. Ranganath. (2023), “Where to Diffuse, How to Diffuse, and How to Get Back: Automated Learning for Multivariate Diffusions”, ICLR

L. H. Zhang, and R. Ranganath. (2023), “Robustness to Spurious Correlations Improves Semantic Out-of-Distribution Detection”, AAAI, pages 15305-15312

A. Puli, L. Zhang, Y. Wald, and R. Ranganath. (2023), “Don’t blame Dataset Shift! Shortcut Learning due to Gradients and Cross Entropy”, arXiv preprint arXiv:2308.12553

N. Jethani, A. Saporta, and R. Ranganath. (2023), “Don’t be fooled: label leakage in explanation methods and the importance of their quantitative evaluation”, International Conference on Artificial Intelligence and Statistics, pages 8925-8953

M. Sudarshan, A. Puli, W. Tansey, and R. Ranganath. (2023), “DIET: Conditional independence testing with marginal dependence measures of residual information”, International Conference on Artificial Intelligence and Statistics, pages 10343-10367

M. S. Albergo, M. Goldstein, N. M. Boffi, R. Ranganath, and E. Vanden-Eijnden. (2023), “Stochastic interpolants with data-dependent couplings”, arXiv preprint arXiv:2310.03725

G. E. Moran, D. M. Blei, and R. Ranganath. (2023), “Holdout predictive checks for Bayesian model criticism”, Journal of the Royal Statistical Society Series B: Statistical Methodology, pages qkad105

W. Amsterdam, and R. Ranganath. (2023), “Conditional average treatment effect estimation with marginally constrained models”, Journal of Causal Inference, Vol. 11, pages 20220027

R. Compton, L. Zhang, A. Puli, and R. Ranganath. (2023), “When More is Less: Incorporating Additional Datasets Can Hurt Performance By Introducing Spurious Correlations”, arXiv preprint arXiv:2308.04431

S. Qi, N. Kumar, M. Farrokh, W. Sun, L. Kuan, R. Ranganath, R. Henao, and R. Greiner. (2023), “An Effective Meaningful Way to Evaluate Survival Models”, ICML, pages 28244-28276

L. Jankelson, N. Jethani, A. Puli, H. Zhang, L. Garber, Y. Aphinyanaphongs, and R. Ranganath. (2023), “PO-04-210 NEW-ONSET DIABETES SCREENING USING ARTIFICIAL INTELLIGENCE-ENHANCED ELECTROCARDIOGRAM”, Heart Rhythm, Vol. 20, pages S617-S618

Y. Hu, A. Lui, M. Goldstein, M. Sudarshan, A. Tinsay, C. Tsui, S. Maidman, J. Medamana, N. Jethani, A. Puli, V. Nguy, Y. Aphinyanaphongs, N. Kiefer, N. Smilowitz, J. Horowitz, T. Ahuja, G. Fishman, J. Hochman, S. Katz, S. Bernard, and R. Ranganath. (2023), “A dynamic risk score for early prediction of cardiogenic shock using machine learning”, arXiv preprint arXiv:2303.12888

N. Murali, A. M. Puli, K. Yu, R. Ranganath, and K. Batmanghelich. (2023), “Shortcut Learning Through the Lens of Early Training Dynamics”, arXiv preprint arXiv:2302.09344

B. M. Lake, and G. L. Murphy. (2023), “Word meaning in minds and machines”, Psychological Review, Vol. 130, pages 401-431

G. Stojnić, K. Gandhi, S. Yasuda, B. M. Lake, and M. R. Dillon. (2023), “Commonsense psychology in human infants and machines”, Cognition, Vol. 235, pages 105406

W. Wang, W. K. Vong, N. Kim, and B. M. Lake. (2023), “Finding Structure in One Child’s Linguistic Experience”, Cognitive science, Vol. 47, pages e13305

Y. Zhou, R. Feinman, and B. M. Lake. (2023), “Compositional diversity in visual concept learning”, arXiv preprint arXiv:2305.19374

A. E. Orhan, and B. M. Lake. (2023), “What can generic neural networks learn from a child’s visual experience?”, arXiv preprint arXiv:2305.15372

B. M. Lake, and M. Baroni. (2023), “Human-like systematic generalization through a meta-learning neural network”, Nature, pages 1-7

A. R. Tartaglini, S. Feucht, M. A. Lepori, W. K. Vong, C. Lovering, B. M. Lake, and E. Pavlick. (2023), “Deep Neural Networks Can Learn Generalizable Same-Different Visual Relations”, arXiv preprint arXiv:2310.09612

A. E. Orhan, and B. M. Lake. (2023), “What can generic neural networks learn from a child’s visual experience?”, arXiv e-prints, pages arXiv: 2305.15372

G. Davidson, A. E. Orhan, and B. M. Lake. (2023), “Spatial Relation Categorization in Infants and Deep Neural Networks”, Available at SSRN 4442327

M. Turpin, J. Michael, E. Perez, and S. R. Bowman. (2023), “Language Models Don’t Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting”, arXiv preprint arXiv:2305.04388

D. Ganguli, A. Askell, N. Schiefer, T. Liao, K. Lukošiūtė, A. Chen, A. Goldie, A. Mirhoseini, C. Olsson, D. Hernandez, D. Drain, D. Li, E. Tran-Johnson, E. Perez, J. Kernion, J. Kerr, J. Mueller, J. Landau, K. Ndousse, K. Nguyen, L. Lovitt, M. Sellitto, N. Elhage, N. Mercado, N. DasSarma, R. Lasenby, R. Larson, S. Ringer, S. Kundu, S. Kadavath, S. Johnston, S. Kravec, S. E. Showk, T. Lanham, T. Telleen-Lawton, T. Henighan, T. Hume, Y. Bai, Z. Hatfield-Dodds, B. Mann, D. Amodei, N. Joseph, S. McCandlish, T. Brown, C. Olah, J. Clark, S. R. Bowman, and J. Kaplan. (2023), “The capacity for moral self-correction in large language models”, arXiv preprint arXiv:2302.07459

T. Korbak, K. Shi, A. Chen, R. Bhalerao, C. L. Buckley, J. Phang, S. R. Bowman, and E. Perez. (2023), “Pretraining language models with human preferences”, ICML 2023

I. R. McKenzie, A. Lyzhov, M. Pieler, A. Parrish, A. Mueller, A. Prabhu, E. McLean, A. Kirtland, A. Ross, A. Liu, A. Gritsevskiy, D. Wurgaft, D. Kauffman, G. Recchia, J. Liu, J. Cavanagh, M. Weiss, S. Huang, T. F. Droid, T. Tseng, T. Korbak, X. Shen, Y. Zhang, Z. Zhou, N. Kim, S. R. Bowman, and E. Perez. (2023), “Inverse Scaling: When Bigger Isn’t Better”, arXiv preprint arXiv:2306.09479

A. Chen, J. Scheurer, T. Korbak, J. A. Campos, J. S. Chan, S. R. Bowman, K. Cho, and E. Perez. (2023), “Improving code generation by training with natural language feedback”, arXiv preprint arXiv:2303.16749

R. Grosse, J. Bae, C. Anil, N. Elhage, A. Tamkin, A. Tajdini, B. Steiner, D. Li, E. Durmus, E. Perez, E. Hubinger, K. Lukošiūtė, K. Nguyen, N. Joseph, S. McCandlish, J. Kaplan, and S. R. Bowman. (2023), “Studying large language model generalization with influence functions”, arXiv preprint arXiv:2308.03296

A. Radhakrishnan, K. Nguyen, A. Chen, C. Chen, C. Denison, D. Hernandez, E. Durmus, E. Hubinger, J. Kernion, K. Lukošiūtė, N. Cheng, N. Joseph, N. Schiefer, O. Rausch, S. McCandlish, S. E. Showk, T. Lanham, T. Maxwell, V. Chandrasekaran, Z. Hatfield-Dodds, J. Kaplan, J. Brauner, S. R. Bowman, and E. Perez. (2023), “Question decomposition improves the faithfulness of model-generated reasoning”, arXiv preprint arXiv:2307.11768

T. Lanham, A. Chen, A. Radhakrishnan, B. Steiner, C. Denison, D. Hernandez, D. Li, E. Durmus, E. Hubinger, J. Kernion, K. Lukošiūtė, K. Nguyen, N. Cheng, N. Joseph, N. Schiefer, O. Rausch, R. Larson, S. McCandlish, S. Kundu, S. Kadavath, S. Yang, T. Henighan, T. Maxwell, T. Telleen-Lawton, T. Hume, Z. Hatfield-Dodds, J. Kaplan, J. Brauner, S. R. Bowman, and E. Perez. (2023), “Measuring faithfulness in chain-of-thought reasoning”, arXiv preprint arXiv:2307.13702

A. Chen, J. Phang, A. Parrish, V. Padmakumar, C. Zhao, S. R. Bowman, and K. Cho. (2023), “Two Failures of Self-Consistency in the Multi-Step Reasoning of LLMs”, arXiv preprint arXiv:2305.14279

J. S. She, C. Potts, S. R. Bowman, and A. Geiger. (2023), “ScoNe: Benchmarking Negation Reasoning in Language Models With Fine-Tuning and In-Context Learning”, ACL, pages 1803-1821

M. Sharma, M. Tong, T. Korbak, D. Duvenaud, A. Askell, S. R. Bowman, N. Cheng, E. Durmus, Z. Hatfield-Dodds, S. R. Johnston, S. Kravec, T. Maxwell, S. McCandlish, K. Ndousse, O. Rausch, N. Schiefer, D. Yan, M. Zhang, and E. Perez. (2023), “Towards Understanding Sycophancy in Language Models”, arXiv preprint arXiv:2310.13548

L. Y. Jiang, X. C. Liu, N. P. Nejatian, M. Nasir-Moin, D. Wang, A. Abidin, K. Eaton, H. A. Riina, I. Laufer, P. Punjabi, M. Miceli, N. C. Kim, C. Orillac, Z. Schnurman, C. Livia, H. Weiss, D. Kurland, S. Neifert, Y. Dastagirzada, D. Kondziolka, A. T. Cheung, G. Yang, M. Cao, M. Flores, A. B. Costa, Y. Aphinyanaphongs, K. Cho, and E. K. Oermann. (2023), “Health system-scale language models are all-purpose prediction engines”, Nature, pages 1-6

J. Scheurer, J. A. Campos, T. Korbak, J. S. Chan, A. Chen, K. Cho, and E. Perez. (2023), “Training language models with language feedback at scale”, arXiv preprint arXiv:2303.16755

M. W. Shen, E. Bengio, E. Hajiramezanali, A. Loukas, K. Cho, and T. Biancalani. (2023), “Towards Understanding and Improving GFlowNet Training”, ICML, pages 30956-30975

R. Lopez, N. Tagasovska, S. Ra, K. Cho, J. Pritchard, and A. Regev. (2023), “Learning causal representations of single cells via sparse mechanism shift modeling”, Conference on Causal Learning and Reasoning, pages 662-691

A. Chen, J. Phang, A. Parrish, V. Padmakumar, C. Zhao, S. R. Bowman, and K. Cho. (2023), “Two Failures of Self-Consistency in the Multi-Step Reasoning of LLMs”, arXiv preprint arXiv:2305.14279

N. C. Frey, D. Berenberg, J. Kleinhenz, I. Hotzel, J. Lafrance-Vanasse, R. L. Kelly, Y. Wu, A. Rajpal, S. Ra, R. Bonneau, K. Cho, A. Loukas, V. Gligorijevic, and S. Saremi. (2023), “Learning protein family manifolds with smoothed energy-based models”, ICLR 2023 Workshop on Physics for Machine Learning

T. Hamamsy, J. T. Morton, R. Blackwell, D. Berenberg, N. Carriero, V. Gligorijevic, C. E. Strauss, J. K. Leman, K. Cho, and R. Bonneau. (2023), “Protein remote homology detection and structural alignment using deep learning”, Nature Biotechnology, pages 1-11

K. Martinkus, J. Ludwiczak, K. Cho, W. Lian, J. Lafrance-Vanasse, I. Hotzel, A. Rajpal, Y. Wu, R. Bonneau, V. Gligorijevic, and A. Loukas. (2023), “Abdiffuser: Full-atom generation of in-vitro functioning antibodies”, arXiv preprint arXiv:2308.05027

R. Y. Pang, S. Roller, K. Cho, H. He, and J. Weston. (2023), “Leveraging Implicit Feedback from Deployment Data in Dialogue”, arXiv preprint arXiv:2307.14117

W. Yuan, K. Cho, and J. Weston. (2023), “System-Level Natural Language Feedback”, arXiv preprint arXiv:2306.13588

H. R. Rajamohan, T. Wang, K. Leung, G. Chang, K. Cho, R. Kijowski, and C. M. Deniz. (2023), “Prediction of total knee replacement using deep learning analysis of knee MRI”, Scientific reports, Vol. 13, pages 6922

N. H. Ng, N. Hulkund, K. Cho, and M. Ghassemi. (2023), “Predicting Out-of-Domain Generalization with Neighborhood Invariance”, Transactions on Machine Learning Research

W. I. Cho, E. Cho, and K. Cho. (2023), “PaperCard for Reporting Machine Assistance in Academic Writing”, arXiv preprint arXiv:2310.04824

A. Chen, R. Schwartz-Ziv, K. Cho, M. L. Leavitt, and N. Saphra. (2023), “Sudden Drops in the Loss: Syntax Acquisition, Phase Transitions, and Simplicity Bias in MLMs”, arXiv preprint arXiv:2309.07311

N. Ng, J. W. Park, J. H. Lee, R. L. Kelly, S. Ra, and K. Cho. (2023), “Blind Biological Sequence Denoising with Self-Supervised Set Learning”, arXiv preprint arXiv:2309.01670

D. J. Im, and K. Cho. (2023), “Active and Passive Causal Inference Learning”, arXiv preprint arXiv:2308.09248

M. Y. Hu, A. Chen, N. Saphra, and K. Cho. (2023), “Latent State Models of Training Dynamics”, arXiv preprint arXiv:2308.09543

H. Zheng, Y. Zhu, L. Y. Jiang, K. Cho, and E. K. Oermann. (2023), “Making the Most Out of the Limited Context Length: Predictive Power Varies with Clinical Note Type and Note Section”, ACL, pages 104-108

B. Can, M. Mozes, S. Cahyawijaya, N. Saphra, N. Kassner, S. Ravfogel, A. Ravichander, C. Zhao, I. Augenstein, A. Rogers, K. Cho, E. Grefenstette, and L. Voita. (2023), “Proceedings of the 8th Workshop on Representation Learning for NLP (RepL4NLP 2023)”, Proceedings of the 8th Workshop on Representation Learning for NLP (RepL4NLP 2023)

Z. Yang, C. Zhang, M. Wu, X. Liu, L. Jiang, K. Cho, and E. Oermann. (2023), “Intriguing Effect of the Correlation Prior on ICD-9 Code Assignment”, Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop), pages 109-118

N. C. Frey, D. Berenberg, K. Zadorozhny, J. Kleinhenz, J. Lafrance-Vanasse, I. Hotzel, Y. Wu, S. Ra, R. Bonneau, K. Cho, A. Loukas, V. Gligorijevic, and S. Saremi. (2023), “Protein Discovery with Discrete Walk-Jump Sampling”, arXiv preprint arXiv:2306.12360

J. W. Park, N. Tagasovska, M. Maser, S. Ra, and K. Cho. (2023), “BOtied: Multi-objective Bayesian optimization with tied multivariate ranks”, arXiv preprint arXiv:2306.00344

T. Makino, Y. Wang, K. J. Geras, and K. Cho. (2023), “Detecting incidental correlation in multimodal learning via latent variable modeling”, Transactions on Machine Learning Research

M. P. Masipa, S. Hassan, M. G. Santos, G. Contardo, and K. Cho. (2023), “Emulating radiation transport on cosmological scale using a denoising Unet”, arXiv preprint arXiv:2303.12065

C. Lee, and K. Cho. (2023), “Unsupervised Learning of Initialization in Deep Neural Networks via Maximum Mean Discrepancy”, arXiv preprint arXiv:2302.04369

Y. Du, C. Durkan, R. Strudel, J. B. Tenenbaum, S. Dieleman, R. Fergus, J. Sohl-Dickstein, A. Doucet, and W. S. Grathwohl. (2023), “Reduce, reuse, recycle: Compositional generation with energy-based diffusion models and mcmc”, International Conference on Machine Learning, pages 8489-8510

I. Dasgupta, C. Kaeser-Chen, K. Marino, A. Ahuja, S. Babayan, F. Hill, and R. Fergus. (2023), “Collaborating with language models for embodied reasoning”, arXiv preprint arXiv:2302.00763

T. Sumers, K. Marino, A. Ahuja, R. Fergus, and I. Dasgupta. (2023), “Distilling internet-scale vision-language models into embodied agents”, ICML, pages 32797-32818

S. Kim, A. S. Rawat, M. Zaheer, S. Jayasumana, V. Sadhanala, W. Jitkrittum, A. K. Menon, R. Fergus, and S. Kumar. (2023), “EmbedDistill: A Geometric Knowledge Distillation for Information Retrieval”, arXiv preprint arXiv:2301.12005

U. Piterbarg, L. Pinto, and R. Fergus. (2023), “NetHack is Hard to Hack”, arXiv preprint arXiv:2305.19240

A. Ahuja, K. Kopparapu, R. Fergus, and I. Dasgupta. (2023), “Hierarchical reinforcement learning with natural language subgoals”, arXiv preprint arXiv:2309.11564

B. Mazoure, J. Bruce, D. Precup, R. Fergus, and A. Anand. (2023), “Accelerating exploration and representation learning with offline pre-training”, arXiv preprint arXiv:2304.00046

J. Bruna, B. Peherstorfer, and E. Vanden-Eijnden. (2023), “Neural Galerkin schemes with active learning for high-dimensional evolution equations”, Journal of Computational Physics, pages 112588

D. Brandfonbrener, O. Nachum, and J. Bruna. (2023), “Inverse Dynamics Pretraining Learns Good Representations for Multitask Imitation”, arXiv preprint arXiv:2305.16985

J. Bruna, L. Pillaud-Vivien, and A. Zweig. (2023), “On single index models beyond gaussian data”, arXiv preprint arXiv:2307.15804

A. Zweig, and J. Bruna. (2023), “Symmetric Single Index Learning”, arXiv preprint arXiv:2310.02117

K. Otness, L. Zanna, and J. Bruna. (2023), “Data-driven multiscale modeling of subgrid parameterizations in climate models”, arXiv preprint arXiv:2303.17496

A. Bietti, J. Bruna, and L. Pillaud-Vivien. (2023), “On Learning Gaussian Multi-index Models with Gradient Flow”, arXiv preprint arXiv:2310.19793

C. Pedersen, L. Zanna, J. Bruna, and P. Perezhogin. (2023), “Reliable coarse-grained turbulent simulations through combined offline learning and neural emulation”, arXiv preprint arXiv:2307.13144

V. Kothapalli, T. Tirer, and J. Bruna. (2023), “A Neural Collapse Perspective on Feature Evolution in Graph Neural Networks”, arXiv preprint arXiv:2307.01951

F. Guth, E. Lempereur, J. Bruna, and S. Mallat. (2023), “Conditionally Strongly Log-Concave Generative Models”, ICML, pages 12224-12251

G. Mialon, R. Dessì, M. Lomeli, C. Nalmpantis, R. Pasunuru, R. Raileanu, B. Rozière, T. Schick, J. Dwivedi-Yu, A. Celikyilmaz, E. Grave, Y. LeCun, and T. Scialom. (2023), “Augmented language models: a survey”, arXiv preprint arXiv:2302.07842

M. Assran, Q. Duval, I. Misra, P. Bojanowski, P. Vincent, M. Rabbat, Y. LeCun, and N. Ballas. (2023), “Self-supervised learning from images with a joint-embedding predictive architecture”, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15619-15629

X. He, B. Hooi, T. Laurent, A. Perold, Y. LeCun, and X. Bresson. (2023), “A generalization of vit/mlp-mixer to graphs”, International Conference on Machine Learning, pages 12724-12745

V. Cabannes, B. Kiani, R. Balestriero, Y. LeCun, and A. Bietti. (2023), “The ssl interplay: Augmentations, inductive bias, and generalization”, International Conference on Machine Learning, pages 3252-3298

Q. Garrido, R. Balestriero, L. Najman, and Y. Lecun. (2023), “Rankme: Assessing the downstream performance of pretrained self-supervised representations by their rank”, International Conference on Machine Learning, pages 10929-10974

Q. Garrido, L. Najman, and Y. Lecun. (2023), “Self-supervised learning of Split Invariant Equivariant representations”, ICML, pages 10975-10996

J. Wang, Y. Chen, S. X. Yu, B. Cheung, and Y. LeCun. (2023), “Compact and Optimal Deep Learning with Recurrent Parameter Generators”, Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 3900-3910

R. Balestriero, and Y. LeCun. (2023), “Police: Provably Optimal Linear Constraint Enforcement For Deep Neural Networks”, ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1-5

V. Cabannes, L. Bottou, Y. Lecun, and R. Balestriero. (2023), “Active Self-Supervised Learning: A Few Low-Cost Relationships Are All You Need”, arXiv preprint arXiv:2303.15256

S. Tong, Y. Chen, Y. Ma, and Y. Lecun. (2023), “EMP-SSL: Towards Self-Supervised Learning in One Training Epoch”, arXiv preprint arXiv:2304.03977

R. Shwartz-Ziv, R. Balestriero, K. Kawaguchi, T. G. Rudner, and Y. LeCun. (2023), “An Information-Theoretic Perspective on Variance-Invariance-Covariance Regularization”, arXiv preprint arXiv:2303.00633

A. Bardes, J. Ponce, and Y. LeCun. (2023), “Mc-jepa: A joint-embedding predictive architecture for self-supervised learning of motion and content features”, arXiv preprint arXiv:2307.12698

A. Dawid, and Y. LeCun. (2023), “Introduction to Latent Variable Energy-Based Models: A Path Towards Autonomous Machine Intelligence”, arXiv preprint arXiv:2306.02572

I. Ben-Shaul, R. Shwartz-Ziv, T. Galanti, S. Dekel, and Y. LeCun. (2023), “Reverse Engineering Self-Supervised Learning”, arXiv preprint arXiv:2305.15614

S. A. Siddiqui, D. Krueger, Y. LeCun, and S. Deny. (2023), “Blockwise self-supervised learning at scale”, arXiv preprint arXiv:2302.01647

G. Mialon, Q. Garrido, H. Lawrence, D. Rehman, Y. LeCun, and B. T. Kiani. (2023), “Self-Supervised Learning with Lie Symmetries for Partial Differential Equations”, arXiv preprint arXiv:2307.05432

R. Balestriero, and Y. LeCun. (2023), “Fast and Exact Enumeration of Deep Networks Partitions Regions”, ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1-5

R. Shwartz-Ziv, R. Balestriero, K. Kawaguchi, T. G. Rudner, and Y. LeCun. (2023), “An Information Theory Perspective on Variance-Invariance-Covariance Regularization”, Thirty-seventh Conference on Neural Information Processing Systems

Z. Yun, J. Zhang, B. Olshausen, Y. LeCun, and Y. Chen. (2023), “URLOST: Unsupervised Representation Learning without Stationarity or Topology”, arXiv preprint arXiv:2310.04496

A. Bar, F. Bordes, A. Shocher, M. Assran, P. Vincent, N. Ballas, T. Darrell, A. Globerson, and Y. LeCun. (2023), “Predicting masked tokens in stochastic locations improves masked image modeling”, arXiv preprint arXiv:2308.00566

J. Zhu, R. Shwartz-Ziv, Y. Chen, and Y. LeCun. (2023), “Variance-Covariance Regularization Improves Representation Learning”, arXiv preprint arXiv:2306.13292

Y. Wang, J. Pfeiffer, N. Carion, Y. LeCun, and A. Kamath. (2023), “Adapting Grounded Visual Question Answering Models to Low Resource Languages”, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2595-2604

S. Yang, Y. Dong, R. Ward, I. S. Dhillon, S. Sanghavi, and Q. Lei. (2023), “Sample efficiency of data augmentation consistency regularization”, International Conference on Artificial Intelligence and Statistics, pages 3825-3853

Z. Wang, J. Lee, and Q. Lei. (2023), “Reconstructing Training Data from Model Gradient, Provably”, International Conference on Artificial Intelligence and Statistics, pages 6595-6612

K. Chua, Q. Lei, and J. Lee. (2023), “Provable Hierarchy-Based Meta-Reinforcement Learning”, International Conference on Artificial Intelligence and Statistics, pages 10918-10967

T. Liu, T. Yang, Q. Zhang, and Q. Lei. (2023), “Optimization for Amortized Inverse Problems”, International Conference on Machine Learning, pages 22289-22319

Y. Dong, K. Miller, Q. Lei, and R. Ward. (2023), “Cluster-aware Semi-supervised Learning: Relational Knowledge Distillation Provably Learns Clustering”, arXiv preprint arXiv:2307.11030

Q. Yu, Y. Wang, B. Huang, Q. Lei, and J. D. Lee. (2023), “Sample Complexity for Quadratic Bandits: Hessian Dependent Bounds and Optimal Algorithms”, arXiv preprint arXiv:2306.12383

Q. Yu, Y. Wang, B. Huang, Q. Lei, and J. D. Lee. (2023), “Optimal Sample Complexity Bounds for Non-convex Optimization under Kurdyka-Lojasiewicz Condition”, International Conference on Artificial Intelligence and Statistics, pages 6806-6821

P. Butlin, R. Long, E. Elmoznino, Y. Bengio, J. Birch, A. Constant, G. Deane, S. M. Fleming, C. Frith, X. Ji, R. Kanai, C. Klein, G. Lindsay, M. Michel, L. Mudrik, M. A. Peters, E. Schwitzgebel, J. Simon, and R. VanRullen. (2023), “Consciousness in artificial intelligence: Insights from the science of consciousness”, arXiv preprint arXiv:2308.08708

G. W. Lindsay, and D. Bau. (2023), “Testing methods of neural systems understanding”, Cognitive Systems Research, Vol. 82, pages 101156

G. Lindsay. (2023), “Invited Session III: Neural network models of the visual system: Exploring the function of different forms of visual recurrence with artificial neural networks”, Journal of vision, Vol. 23, pages 16-16

M. Alakuijala, G. Dulac-Arnold, J. Mairal, J. Ponce, and C. Schmid. (2023), “Learning Reward Functions for Robotic Manipulation by Observing Humans”, Proc. ICRA

A. Bardes, J. Ponce, and Y. LeCun. (2023), “Mc-jepa: A joint-embedding predictive architecture for self-supervised learning of motion and content features”, arXiv preprint arXiv:2307.12698

O. Flasseur, T. Bodrito, J. Mairal, J. Ponce, M. Langlois, and A. Lagrangev. (2023), “Combining multi-spectral data with statistical and deep-learning models for improved exoplanet detection in direct imaging at high contrast”, 2023 31st European Signal Processing Conference (EUSIPCO), pages 1723-1727

O. Flasseur, T. Bodrito, J. Mairal, J. Ponce, M. Langlois, and A. Lagrange. (2023), “deep PACO: Combining statistical models with deep learning for exoplanet detection and characterization in direct imaging at high contrast”, Monthly Notices of the Royal Astronomical Society. Accepted for publication

X. Sun, J. Ponce, and Y. Wang. (2023), “Revisiting Deformable Convolution for Depth Completion”, Proc. IROS

N. Chahine, A. Calarasanu, D. Garcia-Civiero, T. Cayla, S. Ferradans, and J. Ponce. (2023), “An Image Quality Assessment Dataset for Portraits”, Proc. CVPR

E. Vincent, J. Ponce, and M. Aubry. (2023), “Pixel-wise Agricultural Image Time Series Classification: Comparisons and a Deformable Prototype-based Approach”, arXiv preprint arXiv:2303.12533

Y. d. Mont-Marin, J. Ponce, and J. Laumond. (2023), “A minimum swept-volume metric structure for configuration space”, Proc. ICRA

W. Peebles, and S. Xie. (2023), “Scalable diffusion models with transformers”, Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4195-4205

S. Woo, S. Debnath, R. Hu, X. Chen, Z. Liu, I. S. Kweon, and S. Xie. (2023), “Convnext v2: Co-designing and scaling convnets with masked autoencoders”, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16133-16142

H. Xu, S. Xie, P. Huang, L. Yu, R. Howes, G. Ghosh, L. Zettlemoyer, and C. Feichtenhofer. (2023), “Cit: Curation in training for effective vision-language data”, arXiv preprint arXiv:2301.02241

P. Sun, S. Chen, C. Zhu, F. Xiao, P. Luo, S. Xie, and Z. Yan. (2023), “Going Denser with Open-Vocabulary Part Segmentation”, arXiv preprint arXiv:2305.11173

H. Xu, S. Xie, X. E. Tan, P. Huang, R. Howes, V. Sharma, S. Li, G. Ghosh, L. Zettlemoyer, and C. Feichtenhofer. (2023), “Demystifying CLIP Data”, arXiv preprint arXiv:2309.16671

D. L. Barabási, G. Bianconi, E. Bullmore, M. Burgess, S. Chung, T. Eliassi-Rad, D. George, I. A. Kovács, H. Makse, T. E. Nichols, C. Papadimitriou, O. Sporns, K. Stachenfeld, Z. Toroczkai, E. K. Towlson, A. M. Zador, H. Zeng, A. Barabási, A. Bernard, and G. Buzsáki. (2023), “Neuroscience needs Network Science”, Journal of Neuroscience, Vol. 43, pages 5989-5995

J. D. Yao, K. O. Zemlianova, D. L. Hocker, C. Savin, C. M. Constantinople, S. Chung, and D. H. Sanes. (2023), “Transformation of acoustic information to sensory decision variables in the parietal cortex”, Proceedings of the National Academy of Sciences, Vol. 120, pages e2212120120

E. Levy, S. Carrillo-Segura, E. H. Park, W. T. Redman, J. R. Hurtado, S. Chung, and A. A. Fenton. (2023), “A manifold neural population code for space in hippocampal coactivity dynamics independent of place fields”, Cell Reports, Vol. 42

A. Canatar*, J. Feather*, A. Wakhloo, and S. Chung. (2023), “A Spectral Theory of Neural Prediction and Alignment”, NeurIPS

N. Paraouty, J. D. Yao, L. Varnet, C. Chou, S. Chung, and D. H. Sanes. (2023), “Sensory cortex plasticity supports auditory social learning”, Nature Communications, Vol. 14, pages 5828

A. J. Wakhloo, T. J. Sussman, and S. Chung. (2023), “Linear classification of neural manifolds with correlated variability”, Physical Review Letters, Vol. 131, pages 027301

T. Yerxa, Y. Kuang, E. Simoncelli, and S. Chung. (2023), “Learning Efficient Coding of Natural Images with Maximum Manifold Capacity Representations”, NeurIPS

B. C. Weeks, Z. Zhou, B. K. O’Brien, R. Darling, M. Dean, T. Dias, G. Hassena, M. Zhang, and D. F. Fouhey. (2023), “A deep neural network for high‐throughput measurement of functional traits on museum skeletal specimens”, Methods in Ecology and Evolution, Vol. 14, pages 347-359

N. Kulkarni, D. Rempe, K. Genova, A. Kundu, J. Johnson, D. Fouhey, and L. Guibas. (2023), “NIFTY: Neural Object Interaction Fields for Guided Human Motion Synthesis”, arXiv preprint arXiv:2307.07511

B. C. Weeks, C. Harvey, J. A. Tobias, C. Sheard, Z. Zhou, and D. F. Fouhey. (2023), “Skeletal morphology of bird wings is determined by thermoregulatory demand for heat dissipation in warmer climates”, bioRxiv, pages 2023.02. 06.527306

L. Jin, J. Zhang, Y. Hold-Geoffroy, O. Wang, K. Blackburn-Matzen, M. Sticha, and D. F. Fouhey. (2023), “Perspective Fields for Single Image Camera Calibration”, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17307-17316

J. Yang, X. Chen, S. Qian, N. Madaan, M. Iyengar, D. F. Fouhey, and J. Chai. (2023), “LLM-Grounder: Open-Vocabulary 3D Visual Grounding with Large Language Model as an Agent”, arXiv preprint arXiv:2309.12311

V. Tschernezki, A. Darkhalil, Z. Zhu, D. Fouhey, I. Laina, D. Larlus, D. Damen, and A. Vedaldi. (2023), “EPIC Fields: Marrying 3D Geometry and Video Understanding”, arXiv preprint arXiv:2306.08731

S. Qian, and D. F. Fouhey. (2023), “Understanding 3D Object Interaction from a Single Image”, arXiv preprint arXiv:2305.09664

D. F. Fouhey, R. E. Higgins, S. K. Antiochos, G. Barnes, M. L. DeRosa, J. T. Hoeksema, K. Leka, Y. Liu, P. W. Schuck, and T. I. Gombosi. (2023), “Large-scale Spatial Cross-calibration of Hinode/SOT-SP and SDO/HMI”, The Astrophysical Journal Supplement Series, Vol. 264, pages 49

N. Kulkarni, L. Jin, J. Johnson, and D. F. Fouhey. (2023), “Learning To Predict Scene-Level Implicit 3D From Posed RGBD Data”, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17256-17265

R. E. Higgins, and D. F. Fouhey. (2023), “MOVES: Manipulated Objects in Video Enable Segmentation”, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6334-6343

J. Cable, A. Fábrega, S. Park, and M. A. Specter. (2023), “A systematization of voter registration security”, Journal of Cybersecurity, Vol. 9, pages tyad008

2022

M. Ren, S. Kornblith, R. Liao, and G. Hinton. (2022), “Scaling forward gradient with local losses”, ICLR

R. Liao, S. Kornblith, M. Ren, D. J. Fleet, and G. Hinton. (2022), “Gaussian-bernoulli rbms without tears”, arXiv preprint arXiv:2210.10318

A. J. Nam, M. Ren, C. Finn, and J. L. McClelland. (2022), “Learning to reason with relational abstractions”, arXiv preprint arXiv:2210.02615

M. Jones, T. Scott, G. ElSayed, M. Ren, K. Hermann, D. Mayo, and M. C. Mozer. (2022), “Neural network online training with sensitivity to multiscale temporal structure”, NeurIPS workshop on Memory in Artificial and Real Intelligence (MemARI)

R. Singhal, M. Sudarshan, L. Ginocchio, A. Tong, H. Chandarana, D. Sodickson, R. Ranganath, and S. Chopra. (2022), “Accelerated MR screenings with direct k-space classification”, Joint Annual Meeting ISMRM-ESMRMB & ISMRT 31st Annual Meeting, pages 07-12

M. D. Lee, M. Elsayed, S. Chopra, and Y. W. Lui. (2022), “A No-Math Primer on the Principles of Machine Learning for Radiologists”, Seminars in Ultrasound, CT and MRI, Vol. 43, pages 133-141

N. Shafiullah, Z. J. Cui, A. Altanzaya, and L. Pinto. (2022), “Behavior Transformers: Cloning modes with one stone”, NeurIPS

D. Yarats, D. Brandfonbrener, H. Liu, M. Laskin, P. Abbeel, A. Lazaric, and L. Pinto. (2022), “Don’t change the algorithm, change the data: Exploratory data for offline reinforcement learning”, arXiv preprint arXiv:2201.13425

N. Shafiullah, C. Paxton, L. Pinto, S. Chintala, and A. Szlam. (2022), “Clip-fields: Weakly supervised semantic fields for robotic memory”, Robotics – Science and Systems

Z. J. Cui, Y. Wang, N. Shafiullah, and L. Pinto. (2022), “From play to policy: Conditional behavior generation from uncurated robot data”, ICLR

S. Dasari, J. Wang, J. Hong, S. Bahl, Y. Lin, A. Wang, A. Thankaraj, K. Chahal, B. Calli, S. Gupta, D. Held, L. Pinto, D. Pathak, V. Kumar, and A. Gupta. (2022), “Rb2: Robotic manipulation benchmarking with a twist”, arXiv preprint arXiv:2203.08098

B. Evans, A. Thankaraj, and L. Pinto. (2022), “Context is everything: Implicit identification for dynamics adaptation”, 2022 International Conference on Robotics and Automation (ICRA), pages 2642-2648

N. M. Shafiullah, and L. Pinto. (2022), “One after another: Learning incremental skills for a changing world”, ICLR

S. Young, J. Pari, P. Abbeel, and L. Pinto. (2022), “Playful interactions for representation learning”, 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 992-999

A. Thankaraj, and L. Pinto. (2022), “That Sounds Right: Auditory Self-Supervision for Dynamic Robot Manipulation”, arXiv preprint arXiv:2210.01116

W. Han, H. Wu, E. Hirota, A. Gao, L. Pinto, L. Righetti, and C. Feng. (2022), “Learning Simultaneous Navigation and Construction in Grid Worlds”, The Eleventh International Conference on Learning Representations

A. Srivastava, A. Rastogi, A. Rao, A. Shoeb, A. Abid, A. Fisch, A. R. Brown, A. Santoro, A. Gupta, A. Garriga-Alonso, A. Kluska, A. Lewkowycz, A. Agarwal, A. Power, A. Ray, A. Warstadt, A. W. Kocurek, A. Safaya, A. Tazarv, A. Xiang, A. Parrish, A. Nie, A. Hussain, A. Askell, A. Dsouza, A. Slone, A. Rahane, A. S. Iyer, A. Andreassen, A. Madotto, A. Santilli, A. Stuhlmüller, A. Dai, A. La, A. Lampinen, A. Zou, A. Jiang, A. Chen, A. Vuong, A. Gupta, A. Gottardi, A. Norelli, A. Venkatesh, A. Gholamidavoodi, A. Tabassum, A. Menezes, A. Kirubarajan, A. Mullokandov, A. Sabharwal, A. Herrick, A. Efrat, A. Erdem, A. Karakaş, B. R. Roberts, B. S. Loe, B. Zoph, B. Bojanowski, B. Özyurt, B. Hedayatnia, B. Neyshabur, B. Inden, B. Stein, B. Ekmekci, B. Y. Lin, B. Howald, B. Orinion, C. Diao, C. Dour, C. Stinson, C. Argueta, C. F. Ramírez, C. Singh, C. Rathkopf, C. Meng, C. Baral, C. Wu, C. Callison-Burch, C. Waites, C. Voigt, C. D. Manning, C. Potts, C. Ramirez, C. E. Rivera, C. Siro, C. Raffel, C. Ashcraft, C. Garbacea, D. Sileo, D. Garrette, D. Hendrycks, D. Kilman, D. Roth, D. Freeman, D. Khashabi, D. Levy, D. M. González, D. Perszyk, D. Hernandez, D. Chen, D. Ippolito, D. Gilboa, D. Dohan, D. Drakard, D. Jurgens, D. Datta, D. Ganguli, D. Emelin, D. Kleyko, D. Yuret, D. Chen, D. Tam, D. Hupkes, D. Misra, D. Buzan, D. C. Mollo, D. Yang, D. Lee, D. Schrader, E. Shutova, E. D. Cubuk, E. Segal, E. Hagerman, E. Barnes, E. Donoway, E. Pavlick, E. Rodola, E. Lam, E. Chu, E. Tang, E. Erdem, E. Chang, E. A. Chi, E. Dyer, E. Jerzak, E. Kim, E. E. Manyasi, E. Zheltonozhskii, F. Xia, F. Siar, F. Martínez-Plumed, F. Happé, F. Chollet, F. Rong, G. Mishra, G. I. Winata, G. d. Melo, G. Kruszewski, G. Parascandolo, G. Mariani, and G. Wang. (2022), “Beyond the imitation game: Quantifying and extrapolating the capabilities of language models”, arXiv preprint arXiv:2206.04615

N. Dziri, H. Rashkin, T. Linzen, and D. Reitter. (2022), “Evaluating Attribution in Dialogue Systems: The BEGIN Benchmark”, Transactions of the Association for Computational Linguistics, Vol. 10, pages 1066-1083

S. Arehalli, B. Dillon, and T. Linzen. (2022), “Syntactic Surprisal From Neural Models Predicts, But Underestimates, Human Processing Difficulty From Syntactic Ambiguities”, arXiv preprint arXiv:2210.12187

S. Schuster, and T. Linzen. (2022), “When a sentence does not introduce a discourse entity, Transformer-based models still sometimes refer to it”, NAACL-HLT, pages 969-982

A. Mueller, R. Frank, T. Linzen, L. Wang, and S. Schuster. (2022), “Coloring the Blank Slate: Pre-training Imparts a Hierarchical Inductive Bias to Sequence-to-sequence Models”, ACL, pages 1352-1368

N. Kim, T. Linzen, and P. Smolensky. (2022), “Uncontrolled Lexical Exposure Leads to Overestimation of Compositional Generalization in Pretrained Models”, arXiv preprint arXiv:2212.10769

W. Merrill, A. Warstadt, and T. Linzen. (2022), “Entailment Semantics Can Be Extracted from an Ideal Language Model”, arXiv preprint arXiv:2209.12407

K. Armeni, C. Honey, and T. Linzen. (2022), “Characterizing Verbatim Short-Term Memory in Neural Language Models”, arXiv preprint arXiv:2210.13569

A. Mueller, Y. Xia, and T. Linzen. (2022), “Causal Analysis of Syntactic Agreement Neurons in Multilingual Language Models”, arXiv preprint arXiv:2210.14328

A. Kobzeva, S. Arehalli, T. Linzen, and D. Kush. (2022), “LSTMs Can Learn Basic Wh-and Relative Clause Dependencies in Norwegian”, Proceedings of the Annual Meeting of the Cognitive Science Society, Vol. 44

A. Saparov, and H. He. (2022), “Language models are greedy reasoners: A systematic formal analysis of chain-of-thought”, ICLR

S. R. Bowman, A. Chen, H. He, N. Joshi, J. Ma, N. Nangia, V. Padmakumar, R. Y. Pang, A. Parrish, J. Phang, and J. Thompson. (2022), “QuALITY: Question Answering with Long Input Texts, Yes!”, NAACL 2022

T. Chakrabarty, V. Padmakumar, and H. He. (2022), “Help me write a poem: Instruction Tuning as a Vehicle for Collaborative Poetry Writing”, EMNLP, pages 6848-6863

Y. Chen, C. Zhao, Z. Yu, K. McKeown, and H. He. (2022), “On the relation between sensitivity and accuracy in in-context learning”, arXiv preprint arXiv:2209.07661

N. Joshi, X. Pan, and H. He. (2022), “Are all spurious features in natural language alike? an analysis through a causal lens”, EMNLP, pages 9804-9817

V. Padmakumar, L. Lausen, M. Ballesteros, S. Zha, H. He, and G. Karypis. (2022), “Exploring the role of task transferability in large-scale multi-task learning”, NAACL-HLT, pages 2542-2550

A. Puli, N. Joshi, H. He, and R. Ranganath. (2022), “Nuisances via Negativa: Adjusting for Spurious Correlations via Data Augmentation”, arXiv preprint arXiv:2210.01302

Z. Tian, Y. Zhao, Z. Huang, Y. Wang, N. L. Zhang, and H. He. (2022), “SeqPATE: Differentially private text generation via knowledge distillation”, Advances in Neural Information Processing Systems, Vol. 35, pages 11117-11130

T. Wang, F. Ladhak, E. Durmus, and H. He. (2022), “Improving Faithfulness by Augmenting Negative Summaries from Fake Documents”, Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 11913-11921

R. Y. Pang, V. Padmakumar, T. Sellam, A. P. Parikh, and H. He. (2022), “Reward Gaming in Conditional Text Generation”, ACL, pages 4746-4763

C. Liu, M. Picheny, L. Sarı, P. Chitkara, A. Xiao, X. Zhang, M. Chou, A. Alvarado, C. Hazirbas, and Y. Saraf. (2022), “Towards measuring fairness in speech recognition: Casual conversations dataset transcriptions”, ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 6162-6166

C. Peyser, R. Sainath, M. Picheny, and K. Cho. (2022), “Towards disentangled speech representations”, INTERSPEECH, pages 3603-3607

P. Kirichenko, P. Izmailov, and A. G. Wilson. (2022), “Last layer re-training is sufficient for robustness to spurious correlations”, ICLR

P. Izmailov, P. Kirichenko, N. Gruver, and A. G. Wilson. (2022), “On feature learning in the presence of spurious correlations”, Advances in Neural Information Processing Systems, Vol. 35, pages 38516-38532

S. Stanton, W. Maddox, N. Gruver, P. Maffettone, E. Delaney, P. Greenside, and A. G. Wilson. (2022), “Accelerating bayesian optimization for biological sequence design with denoising autoencoders”, International Conference on Machine Learning, pages 20459-20478

S. Lotfi, P. Izmailov, G. Benton, M. Goldblum, and A. G. Wilson. (2022), “Bayesian model selection, the marginal likelihood, and generalization”, International Conference on Machine Learning, pages 14223-14247

J. Venderley, K. Mallayya, M. Matty, M. Krogstad, J. Ruff, G. Pleiss, V. Kishore, D. Mandrus, D. Phelan, L. Poudel, A. G. Wilson, K. Weinberger, P. Upreti, M. Norman, S. Rosenkranz, R. Osborn, and E. Kim. (2022), “Harnessing interpretable and unsupervised machine learning to address big data from modern X-ray diffraction”, Proceedings of the National Academy of Sciences, Vol. 119, pages e2109665119

N. Gruver, M. Finzi, S. Stanton, and A. G. Wilson. (2022), “Deconstructing the inductive biases of hamiltonian neural networks”, ICLR

A. Ghiasi, H. Kazemi, E. Borgnia, S. Reich, M. Shu, M. Goldblum, A. G. Wilson, and T. Goldstein. (2022), “What do vision transformers learn? a visual exploration”, arXiv preprint arXiv:2212.06727

S. Lotfi, M. Finzi, S. Kapoor, A. Potapczynski, M. Goldblum, and A. G. Wilson. (2022), “PAC-bayes compression bounds so tight that they can explain generalization”, Advances in Neural Information Processing Systems, Vol. 35, pages 31459-31473

S. Kapoor, W. J. Maddox, P. Izmailov, and A. G. Wilson. (2022), “On uncertainty, tempering, and data augmentation in bayesian classification”, Advances in Neural Information Processing Systems, Vol. 35, pages 18211-18225

J. Geiping, M. Goldblum, G. Somepalli, R. Shwartz-Ziv, T. Goldstein, and A. G. Wilson. (2022), “How much data are augmentations worth? An investigation into scaling laws, invariance, and implicit regularization”, ICLR

R. Shwartz-Ziv, M. Goldblum, H. Souri, S. Kapoor, C. Zhu, Y. LeCun, and A. G. Wilson. (2022), “Pre-train your loss: Easy bayesian transfer learning with informative priors”, Advances in Neural Information Processing Systems, Vol. 35, pages 27706-27715

N. Gruver, M. Finzi, M. Goldblum, and A. G. Wilson. (2022), “The lie derivative for measuring learned equivariance”, ICLR

R. Levin, V. Cherepanova, A. Schwarzschild, A. Bansal, C. B. Bruss, T. Goldstein, A. G. Wilson, and M. Goldblum. (2022), “Transfer learning with deep tabular models”, ICLR

A. G. Wilson, P. Izmailov, M. D. Hoffman, Y. Gal, Y. Li, M. F. Pradier, S. Vikram, A. Foong, S. Lotfi, and S. Farquhar. (2022), “Evaluating approximate inference in Bayesian deep learning”, NeurIPS 2021 Competitions and Demonstrations Track, pages 113-124

R. Zhang, A. G. Wilson, and C. D. Sa. (2022), “Low-precision stochastic gradient langevin dynamics”, International Conference on Machine Learning, pages 26624-26644

Z. Liu, Z. Tang, X. Shi, A. Zhang, M. Li, A. Shrivastava, and A. G. Wilson. (2022), “Learning Multimodal Data Augmentation in Feature Space”, ICLR

W. J. Maddox, A. Potapcynski, and A. G. Wilson. (2022), “Low-precision arithmetic for fast Gaussian processes”, Uncertainty in Artificial Intelligence, pages 1306-1316

W. Yang, P. Kirichenko, M. Goldblum, and A. G. Wilson. (2022), “Chroma-VAE: Mitigating Shortcut Learning with Generative Classifiers”, Advances in Neural Information Processing Systems, Vol. 35, pages 20351-20365

S. Stanton, W. Maddox, and A. G. Wilson. (2022), “Bayesian optimization with conformal coverage guarantees”, arXiv preprint arXiv:2210.12496

J. Geiping, G. Somepalli, R. Shwartz-Ziv, A. G. Wilson, T. Goldstein, and M. Goldblum. (2022), “How much Data is Augmentation Worth?”, ICML 2022: Workshop on Spurious Correlations, Invariance and Stability

G. Benton, W. Maddox, and A. G. Wilson. (2022), “Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes”, International Conference on Machine Learning, pages 1798-1816

W. Taranto, S. Lederer, Y. Choi, P. Izmailov, A. G. Wilson, S. Nadj-Perge, and E. Kim. (2022), “Unsupervised learning of two-component nematicity from STM data on magic angle bilayer graphene”, arXiv preprint arXiv:2203.04449

R. Ni, P. Chiang, J. Geiping, M. Goldblum, A. G. Wilson, and T. Goldstein. (2022), “K-SAM: Sharpness-Aware Minimization at the Speed of SGD”, arXiv preprint arXiv:2210.12864

G. Detommaso, A. Gasparin, A. Wilson, and C. Archambeau. (2022), “Uncertainty Calibration in Bayesian Neural Networks via Distance-Aware Priors”, arXiv preprint arXiv:2207.08200

Y. Liu, W. Maddox, M. Jovanovic, S. Klemenz, A. Wilson, L. Schoop, and E. Kim. (2022), “Machine-learning discovery of descriptors for Topological Semimetals”, APS March Meeting Abstracts, Vol. 2022, pages T47. 003

A. Puli, N. Joshi, H. He, and R. Ranganath. (2022), “Nuisances via Negativa: Adjusting for Spurious Correlations via Data Augmentation”, arXiv preprint arXiv:2210.01302

M. Goldstein, J. Jacobsen, O. Chau, A. Saporta, A. M. Puli, R. Ranganath, and A. Miller. (2022), “Learning invariant representations with missing data”, Conference on Causal Learning and Reasoning, pages 290-301

X. Han, M. Goldstein, and R. Ranganath. (2022), “Survival mixture density networks”, Machine Learning for Healthcare Conference, pages 224-248

W. Amsterdam, J. J. Verhoeff, N. I. Harlianto, G. A. Bartholomeus, A. M. Puli, P. Jong, T. Leiner, A. Lindert, M. J. Eijkemans, and R. Ranganath. (2022), “Individual treatment effect estimation in the presence of unobserved confounding using proxies: a cohort study in stage III non-small cell lung cancer”, Scientific reports, Vol. 12, pages 5848

L. Zhang, V. Tozzo, J. Higgins, and R. Ranganath. (2022), “Set Norm and Equivariant Skip Connections: Putting the Deep in Deep Sets”, International Conference on Machine Learning, pages 26559-26574

W. Amsterdam, P. Jong, J. J. Verhoeff, T. Leiner, and R. Ranganath. (2022), “Decision making in cancer: Causal questions require causal answers”, arXiv preprint arXiv:2209.07397

N. Jethani, A. Puli, H. Zhang, L. Garber, L. Jankelson, Y. Aphinyanaphongs, and R. Ranganath. (2022), “New-Onset Diabetes Assessment Using Artificial Intelligence-Enhanced Electrocardiography”, arXiv preprint arXiv:2205.02900

W. Amsterdam, and R. Ranganath. (2022), “Conditional average treatment effect estimation with treatment offset models”, arXiv preprint arXiv:2204.13975

A. R. Tartaglini, W. K. Vong, and B. M. Lake. (2022), “A developmentally-inspired examination of shape versus texture bias in machines”, arXiv preprint arXiv:2202.08340

W. K. Vong, and B. M. Lake. (2022), “Cross‐Situational Word Learning With Multimodal Neural Networks”, Cognitive science, Vol. 46, pages e13122

L. Ruis, and B. Lake. (2022), “Improving systematic generalization through modularity and augmentation”, arXiv preprint arXiv:2202.10745

W. K. Vong, and B. M. Lake. (2022), “Few-shot image classification by generating natural language rules”, ACL Workshop on Learning with Natural Language Supervision

G. Davidson, T. M. Gureckis, and B. Lake. (2022), “Creativity, Compositionality, and Common Sense in Human Goal Generation”, Proceedings of the Annual Meeting of the Cognitive Science Society, Vol. 44

W. K. Vong, and B. Lake. (2022), “Categorising images by generating natural language rules”, Proceedings of the Annual Meeting of the Cognitive Science Society, Vol. 44

I. Itzhak, K. Sinha, B. Lake, A. Williams, and D. Hupkes. (2022), “Evaluating locality in NMT models”, Proceedings of the Annual Meeting of the Cognitive Science Society, Vol. 44

Y. Bai, S. Kadavath, S. Kundu, A. Askell, J. Kernion, A. Jones, A. Chen, A. Goldie, A. Mirhoseini, C. McKinnon, C. Chen, C. Olsson, C. Olah, D. Hernandez, D. Drain, D. Ganguli, D. Li, E. Tran-Johnson, E. Perez, J. Kerr, J. Mueller, J. Ladish, J. Landau, K. Ndousse, K. Lukosuite, L. Lovitt, M. Sellitto, N. Elhage, N. Schiefer, N. Mercado, N. DasSarma, R. Lasenby, R. Larson, S. Ringer, S. Johnston, S. Kravec, S. E. Showk, S. Fort, T. Lanham, T. Telleen-Lawton, T. Conerly, T. Henighan, T. Hume, S. R. Bowman, Z. Hatfield-Dodds, B. Mann, D. Amodei, N. Joseph, S. McCandlish, T. Brown, and J. Kaplan. (2022), “Constitutional AI: Harmlessness from AI feedback”, arXiv preprint arXiv:2212.08073

D. Ganguli, L. Lovitt, J. Kernion, A. Askell, Y. Bai, S. Kadavath, B. Mann, E. Perez, N. Schiefer, K. Ndousse, A. Jones, S. Bowman, A. Chen, T. Conerly, N. DasSarma, D. Drain, N. Elhage, S. El-Showk, S. Fort, Z. Hatfield-Dodds, T. Henighan, D. Hernandez, T. Hume, J. Jacobson, S. Johnston, S. Kravec, C. Olsson, S. Ringer, E. Tran-Johnson, D. Amodei, T. Brown, N. Joseph, S. McCandlish, C. Olah, J. Kaplan, and J. Clark. (2022), “Red teaming language models to reduce harms: Methods, scaling behaviors, and lessons learned”, arXiv preprint arXiv:2209.07858

S. Kadavath, T. Conerly, A. Askell, T. Henighan, D. Drain, E. Perez, N. Schiefer, Z. Hatfield-Dodds, N. DasSarma, E. Tran-Johnson, S. Johnston, S. El-Showk, A. Jones, N. Elhage, T. Hume, A. Chen, Y. Bai, S. Bowman, S. Fort, D. Ganguli, D. Hernandez, J. Jacobson, J. Kernion, S. Kravec, L. Lovitt, K. Ndousse, C. Olsson, S. Ringer, D. Amodei, T. Brown, J. Clark, N. Joseph, B. Mann, S. McCandlish, C. Olah, and J. Kaplan. (2022), “Language models (mostly) know what they know”, arXiv preprint arXiv:2207.05221

A. Parrish, A. Chen, N. Nangia, V. Padmakumar, J. Phang, J. Thompson, P. M. Htut, and S. R. Bowman. (2022), “BBQ: A Hand-Built Bias Benchmark for Question Answering”, Findings of ACL

E. Perez, S. Ringer, K. Lukošiūtė, K. Nguyen, E. Chen, S. Heiner, C. Pettit, C. Olsson, S. Kundu, S. Kadavath, A. Jones, A. Chen, B. Mann, B. Israel, B. Seethor, C. McKinnon, C. Olah, D. Yan, D. Amodei, D. Amodei, D. Drain, D. Li, E. Tran-Johnson, G. Khundadze, J. Kernion, J. Landis, J. Kerr, J. Mueller, J. Hyun, J. Landau, K. Ndousse, L. Goldberg, L. Lovitt, M. Lucas, M. Sellitto, M. Zhang, N. Kingsland, N. Elhage, N. Joseph, N. Mercado, N. DasSarma, O. Rausch, R. Larson, S. McCandlish, S. Johnston, S. Kravec, S. E. Showk, T. Lanham, T. Telleen-Lawton, T. Brown, T. Henighan, T. Hume, Y. Bai, Z. Hatfield-Dodds, J. Clark, S. R. Bowman, A. Askell, R. Grosse, D. Hernandez, D. Ganguli, E. Hubinger, N. Schiefer, and K. Kaplan. (2022), “Discovering Language Model Behaviors with Model-Written Evaluations”, ACL 2023

A. Warstadt, and S. R. Bowman. (2022), “What artificial neural networks can tell us about human language acquisition”, Algebraic Structures in Natural Language, pages 17-60

S. Bowman. (2022), “The Dangers of Underclaiming: Reasons for Caution When Reporting How NLP Systems Fail”, Proceedings of ACL, pages 7484-7499

R. Y. Pang, A. Parrish, N. Joshi, N. Nangia, J. Phang, A. Chen, V. Padmakumar, J. Ma, J. Thompson, H. He, and S. R. Bowman. (2022), “QuALITY: Question Answering with Long Input Texts, Yes!”, Proceedings of NAACL

O. Honovich, U. Shaham, S. R. Bowman, and O. Levy. (2022), “Instruction induction: From few examples to natural language task descriptions”, ACL 2023

S. R. Bowman, J. Hyun, E. Perez, E. Chen, C. Pettit, S. Heiner, K. Lukosuite, A. Askell, A. Jones, A. Chen, A. Goldie, A. Mirhoseini, C. McKinnon, C. Olah, D. Amodei, D. Amodei, D. Drain, D. Li, E. Tran-Johnson, J. Kernion, J. Kerr, J. Mueller, J. Ladish, J. Landau, K. Ndousse, L. Lovitt, N. Elhage, N. Schiefer, N. Joseph, N. Mercado, N. DasSarma, R. Larson, S. McCandlish, S. Kundu, S. Johnston, S. Kravec, S. E. Showk, S. Fort, T. Telleen-Lawton, T. Brown, T. Henighan, T. Hume, Y. Bai, Z. Hatfield-Dodds, B. Mann, and J. Kaplan. (2022), “Measuring progress on scalable oversight for large language models”, arXiv preprint arXiv:2211.03540

E. A. Hosseini, M. Schrimpf, Y. Zhang, S. Bowman, N. Zaslavsky, and E. Fedorenko. (2022), “Artificial neural network language models align neurally and behaviorally with humans even after a developmentally realistic amount of training”, BioRxiv, pages 2022.10. 04.510681

J. Michael, A. Holtzman, A. Parrish, A. Mueller, A. Wang, A. Chen, D. Madaan, N. Nangia, R. Y. Pang, J. Phang, and S. R. Bowman. (2022), “What do NLP researchers believe? Results of the NLP community metasurvey”, ACL 2023

N. Kim, P. M. Htut, S. R. Bowman, and J. Petty. (2022), “: Question Answering with Questionable Assumptions”, ACL 2023

A. Wang, R. Y. Pang, A. Chen, J. Phang, and S. R. Bowman. (2022), “SQuALITY: Building a long-document summarization dataset the hard way”, Findings of EMNLP

A. Parrish, H. Trivedi, E. Perez, A. Chen, N. Nangia, J. Phang, and S. R. Bowman. (2022), “Single-Turn Debate Does Not Help Humans Answer Hard Reading-Comprehension Questions”, Proceedings of LNLS

S. Sugawara, N. Nangia, A. Warstadt, and S. R. Bowman. (2022), “What Makes Reading Comprehension Questions Difficult?”, Proceedings of ACL

A. Parrish, H. Trivedi, N. Nangia, V. Padmakumar, J. Phang, A. S. Saimbhi, and S. R. Bowman. (2022), “Two-Turn Debate Doesn’t Help Humans Answer Hard Reading Comprehension Questions”, NeurIPS ML Safety Workshop

A. Lauscher, F. Bianchi, S. Bowman, and D. Hovy. (2022), “SocioProbe: What, when, and where language models learn about sociodemographics”, EMNLP, pages 7901-7918

C. Edwards, T. Lai, K. Ros, G. Honke, K. Cho, and H. Ji. (2022), “Translation between molecules and natural language”, EMNLP, pages 375-413

S. Shin, S. Lee, H. Ahn, S. Kim, H. Kim, B. Kim, K. Cho, G. Lee, W. Park, J. Ha, and N. Sung. (2022), “On the effect of pretraining corpora on in-context learning by a large-scale language model”, NAACL-HLT, pages 5168-5186

N. Wu, S. Jastrzebski, K. Cho, and K. J. Geras. (2022), “Characterizing and overcoming the greedy nature of learning in multi-modal deep neural networks”, International Conference on Machine Learning, pages 24043-24055

J. Juneja, R. Bansal, K. Cho, J. Sedoc, and N. Saphra. (2022), “Linear connectivity reveals generalization strategies”, ICLR

J. A. Campos, and J. Shern. (2022), “Training language models with language feedback”, ACL Workshop on Learning with Natural Language Supervision. 2022.

T. Makino, S. Jastrzębski, W. Oleszkiewicz, C. Chacko, R. Ehrenpreis, N. Samreen, C. Chhor, E. Kim, J. Lee, K. Pysarenko, B. Reig, H. Toth, D. Awal, L. Du, A. Kim, J. Park, D. K. Sodickson, L. Heacock, L. Moy, K. Cho, and K. J. Geras. (2022), “Differences between human and machine perception in medical diagnosis”, Scientific reports, Vol. 12, pages 6877

J. Scheurer, J. A. Campos, J. S. Chan, A. Chen, K. Cho, and E. Perez. (2022), “Training language models with natural language feedback”, arXiv preprint arXiv:2204.14146, Vol. 8

J. H. Lee, P. Yadollahpour, A. Watkins, N. C. Frey, A. Leaver-Fay, S. Ra, K. Cho, V. Gligorijević, A. Regev, and R. Bonneau. (2022), “Equifold: Protein structure prediction with a novel coarse-grained structure representation”, bioRxiv, pages 2022.10. 07.511322

T. He, J. Zhang, T. Wang, S. Kumar, K. Cho, J. Glass, and Y. Tsvetkov. (2022), “On the blind spots of model-based evaluation metrics for text generation”, ACL, pages 12067-12097

J. W. Park, S. Stanton, S. Saremi, A. Watkins, H. Dwyer, V. Gligorijevic, R. Bonneau, S. Ra, and K. Cho. (2022), “PropertyDAG: Multi-objective Bayesian optimization of partially ordered, mixed-variable properties for biological sequence design”, arXiv preprint arXiv:2210.04096

N. Huang, Y. R. Deshpande, Y. Liu, H. Alberts, K. Cho, C. Vania, and I. Calixto. (2022), “Endowing language models with multimodal knowledge graph representations”, arXiv preprint arXiv:2206.13163

T. Hamamsy, J. T. Morton, D. Berenberg, N. Carriero, V. Gligorijevic, R. Blackwell, C. E. Strauss, J. K. Leman, K. Cho, and R. Bonneau. (2022), “TM-Vec: template modeling vectors for fast homology detection and alignment”, bioRxiv, pages 2022.07. 25.501437

N. Ng, K. Cho, N. Hulkund, and M. Ghassemi. (2022), “Predicting out-of-domain generalization with local manifold smoothness”, arXiv preprint arXiv:2207.02093

V. Sobal, A. Canziani, N. Carion, K. Cho, and Y. LeCun. (2022), “Separating the world and ego models for self-driving”, arXiv preprint arXiv:2204.07184

H. Yoo, J. Jin, J. Son, J. Bak, K. Cho, and A. Oh. (2022), “HUE: Pretrained Model and Dataset for Understanding Hanja Documents of Ancient Korea”, NAACL-HLT, pages 1832-1844

N. Tagasovska, N. C. Frey, A. Loukas, I. Hötzel, J. Lafrance-Vanasse, R. L. Kelly, Y. Wu, A. Rajpal, R. Bonneau, K. Cho, S. Ra, and V. Gligorijević. (2022), “A Pareto-optimal compositional energy-based model for sampling and optimization of protein sequences”, arXiv preprint arXiv:2210.10838

T. Makino, K. Geras, and K. Cho. (2022), “Generative multitask learning mitigates target-causing confounding”, Advances in Neural Information Processing Systems, Vol. 35, pages 36546-36558

C. Peyser, R. Sainath, M. Picheny, and K. Cho. (2022), “Towards disentangled speech representations”, INTERSPEECH, pages 3603-3607

D. Berenberg, J. H. Lee, S. Kelow, J. W. Park, A. Watkins, V. Gligorijević, R. Bonneau, S. Ra, and K. Cho. (2022), “Multi-segment preserving sampling for deep manifold sampler”, arXiv preprint arXiv:2205.04259

J. Son, J. Jin, H. Yoo, J. Bak, K. Cho, and A. Oh. (2022), “Translating Hanja Historical Documents to Contemporary Korean and English”, Findings of the Association for Computational Linguistics: EMNLP 2022, pages 1260-1272

V. Sobal, J. SV, S. Jalagam, N. Carion, K. Cho, and Y. LeCun. (2022), “Joint embedding predictive architectures focus on slow features”, arXiv preprint arXiv:2211.10831

G. Yang, M. Cao, L. Y. Jiang, X. C. Liu, A. Cheung, H. Weiss, D. Kurland, K. Cho, and E. K. Oermann. (2022), “Language model classifier aligns better with physician word sensitivity than XGBoost on readmission prediction”, arXiv preprint arXiv:2211.07047

T. Makino, K. J. Geras, and K. Cho. (2022), “Mitigating input-causing confounding in multimodal learning via the backdoor adjustment”, NeurIPS 2022 Workshop on Causality for Real-world Impact

R. Yi, K. Cho, and R. Bonneau. (2022), “NetTIME: a multitask and base-pair resolution framework for improved transcription factor binding site prediction”, Bioinformatics, Vol. 38, pages 4762-4770

S. Lee, S. Kim, D. Ko, D. Ham, Y. Hong, S. A. Oh, H. Jung, W. Jung, K. Cho, D. Kwak, H. Noh, and W. Park. (2022), “Can Current Task-oriented Dialogue Models Automate Real-world Scenarios in the Wild?”, arXiv preprint arXiv:2212.10504

J. Scheurer, J. A. Campos, J. S. Chan, A. Chen, K. Cho, and E. Perez. (2022), “Learning from Natural Language Feedback”, ACL Workshop on Learning with Natural Language Supervision

E. Choi, K. Cho, and C. Lee. (2022), “A Non-monotonic Self-terminating Language Model”, ICLR

O. Mahmood, C. S. Gibbs, R. Bonneau, and K. Cho. (2022), “A Variational Inference Approach to Single-Cell Gene Regulatory Network Inference using Probabilistic Matrix Factorization”, bioRxiv, pages 2022.09. 09.507305

C. Resnick, O. Litany, A. Kar, K. Kreis, J. Lucas, K. Cho, and S. Fidler. (2022), “Causal Scene BERT: Improving object detection by searching for challenging groups of data”, arXiv preprint arXiv:2202.03651

M. Barot, V. Gligorijevic, R. Bonneau, and K. Cho. (2022), “Automated Protein Function Description for Novel Class Discovery”, bioRxiv, pages 2022.10. 13.512154

J. Bruce, A. Anand, B. Mazoure, and R. Fergus. (2022), “Learning about progress from experts”, The Eleventh International Conference on Learning Representations

M. Zaheer, K. Marino, W. Grathwohl, J. Schultz, W. Shang, S. Babayan, A. Ahuja, I. Dasgupta, C. Kaeser-Chen, and R. Fergus. (2022), “Learning to Navigate Wikipedia by Taking Random Walks”, Advances in Neural Information Processing Systems, Vol. 35, pages 1529-1541

M. Zaheer, A. S. Rawat, S. Kim, C. You, H. Jain, A. Veit, R. Fergus, and S. Kumar. (2022), “Teacher Guided Training: An Efficient Framework for Knowledge Transfer”, ICLR

T. Tirer, and J. Bruna. (2022), “Extended unconstrained features model for exploring deep neural collapse”, International Conference on Machine Learning, pages 21478-21505

F. Williams, Z. Gojcic, S. Khamis, D. Zorin, J. Bruna, S. Fidler, and O. Litany. (2022), “Neural fields as learnable kernels for 3d reconstruction”, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18500-18510

D. Brandfonbrener, A. Bietti, J. Buckman, R. Laroche, and J. Bruna. (2022), “When does return-conditioned supervised learning work for offline reinforcement learning?”, Advances in Neural Information Processing Systems, Vol. 35, pages 1542-1553

A. Bietti, J. Bruna, C. Sanford, and M. J. Song. (2022), “Learning single-index models with shallow neural networks”, Advances in Neural Information Processing Systems, Vol. 35, pages 9768-9783

I. Zadik, M. J. Song, A. S. Wein, and J. Bruna. (2022), “Lattice-based methods surpass sum-of-squares in clustering”, Conference on Learning Theory, pages 1247-1248

L. Chen, and J. Bruna. (2022), “On gradient descent convergence beyond the edge of stability”, arXiv preprint arXiv:2206.04172

Z. Chen, E. Vanden-Eijnden, and J. Bruna. (2022), “On feature learning in neural networks with global convergence guarantees”, ICLR

L. Venturi, S. Jelassi, T. Ozuch, and J. Bruna. (2022), “Depth separation beyond radial functions”, The Journal of Machine Learning Research, Vol. 23, pages 5309-5364

T. Tirer, J. Bruna, and R. Giryes. (2022), “Kernel-based smoothness analysis of residual networks”, Mathematical and Scientific Machine Learning, pages 921-954

S. Zafeiriou, M. Bronstein, T. Cohen, O. Vinyals, L. Song, J. Leskovec, P. Lio, J. Bruna, and M. Gori. (2022), “Guest editorial: Non-euclidean machine learning”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 44, pages 723-726

P. Batlle, J. Bruna, C. Fernandez-Granda, and V. M. Preciado. (2022), “Adaptive test allocation for outbreak detection and tracking in social contact networks”, SIAM Journal on Control and Optimization, Vol. 60, pages S274-S293

A. Zweig, and J. Bruna. (2022), “Exponential separations in symmetric neural networks”, Advances in Neural Information Processing Systems, Vol. 35, pages 33134-33145

J. Bruna. (2022), “The Scattering Transform”, Mathematical Aspects of Deep Learning, pages 338

Z. Chen, E. Vanden-Eijnden, and J. Bruna. (2022), “A Functional-Space Mean-Field Theory of Partially-Trained Three-Layer Neural Networks”, arXiv preprint arXiv:2210.16286

A. Zweig, and J. Bruna. (2022), “Towards Antisymmetric Neural Ansatz Separation”, arXiv preprint arXiv:2208.03264

C. Domingo-Enrich, and J. Bruna. (2022), “Simultaneous transport evolution for minimax equilibria on measures”, arXiv preprint arXiv:2202.06460

G. Sergeant-Perthuis, J. Maier, J. Bruna, and E. Oyallon. (2022), “On Non-Linear operators for Geometric Deep Learning”, Advances in Neural Information Processing Systems, Vol. 35, pages 10984-10995

Y. LeCun. (2022), “A path towards autonomous machine intelligence version 0.9. 2, 2022-06-27”, Open Review, Vol. 62

R. Balestriero, and Y. LeCun. (2022), “Contrastive and non-contrastive self-supervised learning recover global and local spectral embedding methods”, Advances in Neural Information Processing Systems, Vol. 35, pages 26671-26685

R. Balestriero, L. Bottou, and Y. LeCun. (2022), “The effects of regularization and data augmentation are class dependent”, Advances in Neural Information Processing Systems, Vol. 35, pages 37878-37891

A. Bardes, J. Ponce, and Y. LeCun. (2022), “Vicregl: Self-supervised learning of local visual features”, Advances in Neural Information Processing Systems, Vol. 35, pages 8799-8810

Z. Dou, A. Kamath, Z. Gan, P. Zhang, J. Wang, L. Li, Z. Liu, C. Liu, Y. LeCun, N. Peng, J. Gao, and L. Wang. (2022), “Coarse-to-fine vision-language pre-training with fusion in the backbone”, Advances in neural information processing systems, Vol. 35, pages 32942-32956

Q. Garrido, Y. Chen, A. Bardes, L. Najman, and Y. Lecun. (2022), “On the duality between contrastive and non-contrastive self-supervised learning”, ICLR

A. Zador, S. Escola, B. Richards, B. Ölveczky, Y. Bengio, K. Boahen, M. Botvinick, D. Chklovskii, A. Churchland, C. Clopath, J. DiCarlo, S. Ganguli, J. Hawkins, K. Koerding, A. Koulakov, Y. LeCun, T. Lillicrap, A. Marblestone, B. Olshausen, A. Pouget, C. Savin, T. Sejnowski, E. Simoncelli, S. Solla, D. Sussillo, A. S. Tolias, and D. Tsao. (2022), “Toward next-generation artificial intelligence: Catalyzing the neuroai revolution”, arXiv preprint arXiv:2210.08340

Z. Li, Y. Chen, Y. LeCun, and F. T. Sommer. (2022), “Neural manifold clustering and embedding”, arXiv preprint arXiv:2201.10000

L. Jing, J. Zhu, and Y. LeCun. (2022), “Masked siamese convnets”, arXiv preprint arXiv:2206.07700

R. Shwartz-Ziv, R. Balestriero, and Y. LeCun. (2022), “What Do We Maximize in Self-Supervised Learning?”, arXiv preprint arXiv:2207.10081

R. Balestriero, I. Misra, and Y. LeCun. (2022), “A data-augmentation is worth a thousand samples: Exact quantification from analytical augmented sample moments”, arXiv preprint arXiv:2202.08325

Y. Chen, A. Bardes, Z. Li, and Y. LeCun. (2022), “Bag of Image Patch Embedding Behind the Success of Self-Supervised Learning”, arXiv preprint arXiv:2206.08954

B. Kiani, R. Balestriero, Y. LeCun, and S. Lloyd. (2022), “projUNN: Efficient method for training deep networks with unitary matrices”, Advances in Neural Information Processing Systems, Vol. 35, pages 14448-14463

B. T. Kiani, R. Balestriero, Y. Chen, S. Lloyd, and Y. LeCun. (2022), “Joint embedding self-supervised learning in the kernel regime”, arXiv preprint arXiv:2209.14884

V. Sobal, A. Canziani, N. Carion, K. Cho, and Y. LeCun. (2022), “Separating the world and ego models for self-driving”, arXiv preprint arXiv:2204.07184

R. Balestriero, I. Misra, and Y. LeCun. (2022), “A Data-Augmentation Is Worth A Thousand Samples: Analytical Moments And Sampling-Free Training”, Advances in Neural Information Processing Systems, Vol. 35, pages 19631-19644

G. Mialon, R. Balestriero, and Y. LeCun. (2022), “Variance covariance regularization enforces pairwise independence in self-supervised representations”, arXiv preprint arXiv:2209.14905

S. Pramanick, L. Jing, S. Nag, J. Zhu, H. Shah, Y. LeCun, and R. Chellappa. (2022), “VoLTA: Vision-Language Transformer with Weakly-Supervised Local-Feature Alignment”, arXiv preprint arXiv:2210.04135

W. Zhang, A. GX-Chen, V. Sobal, Y. LeCun, and N. Carion. (2022), “Light-weight probing of unsupervised representations for reinforcement learning”, arXiv preprint arXiv:2208.12345

J. Zhu, R. M. Moraes, S. Karakulak, V. Sobol, A. Canziani, and Y. LeCun. (2022), “TiCo: Transformation Invariance and Covariance Contrast for Self-Supervised Visual Representation Learning”, arXiv preprint arXiv:2206.10698

S. Tong, X. Dai, Y. Chen, M. Li, Z. Li, B. Yi, Y. LeCun, and Y. Ma. (2022), “Unsupervised learning of structured representations via closed-loop transcription”, arXiv preprint arXiv:2210.16782

Y. Chen, Z. Yun, Y. Ma, B. Olshausen, and Y. LeCun. (2022), “Minimalistic unsupervised learning with the sparse manifold transform”, ICLR

V. Sobal, J. SV, S. Jalagam, N. Carion, K. Cho, and Y. LeCun. (2022), “Joint embedding predictive architectures focus on slow features”, arXiv preprint arXiv:2211.10831

Y. Chen, Z. Yun, Y. Ma, B. Olshausen, and Y. LeCun. (2022), “Minimalistic Unsupervised Representation Learning with the Sparse Manifold Transform”, The Eleventh International Conference on Learning Representations

M. Cheng, Q. Lei, P. Chen, I. Dhillon, and C. Hsieh. (2022), “Cat: Customized adversarial training for improved robustness”, IJCAI, pages 673-679

J. Yang, Q. Lei, J. D. Lee, and S. S. Du. (2022), “Nearly minimax algorithms for linear bandits with shared representation”, arXiv preprint arXiv:2203.15664

G. W. Lindsay, T. D. Mrsic-Flogel, and M. Sahani. (2022), “Bio-inspired neural networks implement different recurrent visual processing strategies than task-trained ones do”, bioRxiv, pages 2022.03. 07.483196

G. W. Lindsay. (2022), “Testing the tools of systems neuroscience on artificial neural networks”, arXiv preprint arXiv:2202.07035

A. Bardes, J. Ponce, and Y. LeCun. (2022), “Vicreg: Variance-invariance-covariance regularization for self-supervised learning”, Proc. ICLR

A. Bardes, J. Ponce, and Y. LeCun. (2022), “Vicregl: Self-supervised learning of local visual features”, Proc. NeurIPS

B. Lecouat, T. Eboli, J. Ponce, and J. Mairal. (2022), “High dynamic range and super-resolution from raw image bursts”, ACM Transactions on Graphics (SIGGRAPH), Vol. 41, pages 1-21

H. V. Vo, O. Siméoni, S. Gidaris, A. Bursuc, P. Pérez, and J. Ponce. (2022), “Active Learning Strategies for Weakly-Supervised Object Detection”, Proc. ECCV

T. Chabal, R. Strudel, E. Arlaud, J. Ponce, and C. Schmid. (2022), “Assembly Planning from Observations under Physical Constraints”, Proc. IROS

G. L. Moing, J. Ponce, and C. Schmid. (2022), “WALDO: Future Video Synthesis using Object Layer Decomposition and Parametric Flow Prediction”, Proc. ICCV 2023

O. Bounou, J. Ponce, and J. Carpentier. (2022), “Leveraging Proximal Optimization for Differentiating Optimal Control Solvers”, Proc. IEEE Conference on Decision and Control 2023

O. Flasseur, T. Bodrito, J. Mairal, J. Ponce, M. Langlois, and A. Lagrange. (2022), “Exoplanet detection in angular differential imaging: combining a statistics-based learning with a deep-based learning for improved detections”, Adaptive Optics Systems VIII, Vol. 12185, pages 1154-1167

K. He, X. Chen, S. Xie, Y. Li, P. Dollár, and R. Girshick. (2022), “Masked autoencoders are scalable vision learners”, Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 16000-16009

Z. Liu, H. Mao, C. Wu, C. Feichtenhofer, T. Darrell, and S. Xie. (2022), “A convnet for the 2020s”, Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11976-11986

C. Wei, H. Fan, S. Xie, C. Wu, A. Yuille, and C. Feichtenhofer. (2022), “Masked feature prediction for self-supervised visual pre-training”, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14668-14678

A. Tejankar, M. Sanjabi, B. Wu, M. Khabsa, S. Xie, H. Pirsiavash, and H. Firooz. (2022), “Can we train vision and language zero-shot classification models without syntax?”, NeurIPS 2022 Workshop: Self-Supervised Learning-Theory and Practice, 2022

R. Hu, S. Debnath, S. Xie, and X. Chen. (2022), “Exploring long-sequence masked autoencoders”, arXiv preprint arXiv:2210.07224

2021

W. Yan, A. Vangipuram, P. Abbeel, and L. Pinto. (2021), “Learning predictive representations for deformable objects using contrastive estimation”, Conference on Robot Learning, pages 564-574

D. Yarats, R. Fergus, A. Lazaric, and L. Pinto. (2021), “Mastering visual continuous control: Improved data-augmented reinforcement learning”, ICLR

D. Yarats, R. Fergus, A. Lazaric, and L. Pinto. (2021), “Reinforcement learning with prototypical representations”, International Conference on Machine Learning, pages 11920-11931

A. Zhan, R. Zhao, L. Pinto, P. Abbeel, and M. Laskin. (2021), “A framework for efficient robotic manipulation”, Deep RL Workshop NeurIPS 2021

M. Laskin, D. Yarats, H. Liu, K. Lee, A. Zhan, K. Lu, C. Cang, L. Pinto, and P. Abbeel. (2021), “URLB: Unsupervised reinforcement learning benchmark”, NeurIPS Datasets and Benchmarks

I. Radosavovic, X. Wang, L. Pinto, and J. Malik. (2021), “State-only imitation learning for dexterous manipulation”, 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 7865-7871

J. Pari, N. M. Shafiullah, S. P. Arunachalam, and L. Pinto. (2021), “The surprising effectiveness of representation learning for visual imitation”, Robotics – Science and Systems

S. Young, D. Gandhi, S. Tulsiani, A. Gupta, P. Abbeel, and L. Pinto. (2021), “Visual imitation made easy”, Conference on Robot Learning, pages 1992-2005

D. J. Hejna III, P. Abbeel, and L. Pinto. (2021), “Task-agnostic morphology evolution”, ICLR

T. Linzen, and M. Baroni. (2021), “Syntactic Structure from Deep Learning”, Annual Reviews of Linguistics

C. Lovering, R. Jha, T. Linzen, and E. Pavlick. (2021), “Predicting Inductive Biases of Pre-trained Models”, ICLR

T. Sellam, S. Yadlowsky, J. Wei, N. Saphra, A. D’Amour, T. Linzen, J. Bastings, I. Turc, J. Eisenstein, D. Das, I. Tenney, and E. Pavlick. (2021), “The MultiBERTs: BERT Reproductions for Robustness Analysis”, ICLR 2022

J. Wei, D. Garrette, T. Linzen, and E. Pavlick. (2021), “Frequency Effects on Syntactic Rule Learning in Transformers”, EMNLP 2021

M. Finlayson, A. Mueller, S. Shieber, S. Gehrmann, T. Linzen, and Y. Belinkov. (2021), “Causal Analysis of Syntactic Agreement Mechanisms in Neural Language Models”, Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 1828–1843

M. V. Schijndel, and T. Linzen. (2021), “Single‐Stage Prediction Models Do Not Explain the Magnitude of Syntactic Disambiguation Difficulty”, Cognitive Science, Vol. 45, pages e12988

G. Prasad, and T. Linzen. (2021), “Rapid syntactic adaptation in self-paced reading: Detectable, but only with many participants.”, Journal of Experimental Psychology: Learning, Memory, and Cognition, Vol. 47, pages 1156

L. Qiu, P. Shaw, P. Pasupat, P. K. Nowak, T. Linzen, F. Sha, and K. Toutanova. (2021), “Improving Compositional Generalization with Latent Structure and Data Augmentation”, NAACL-HLT, pages 4341-4362

S. Ravfogel, G. Prasad, T. Linzen, and Y. Goldberg. (2021), “Counterfactual Interventions Reveal the Causal Effect of Relative Clause Representations on Agreement Prediction”, CoNLL 2021

A. Parrish, W. Huang, O. Agha, S. Lee, N. Nangia, A. Warstadt, K. Aggarwal, E. Allaway, T. Linzen, and S. R. Bowman. (2021), “Does Putting a Linguist in the Loop Improve NLU Data Collection?”, CoNLL 2021

A. Parrish, S. Schuster, A. Warstadt, O. Agha, S. Lee, Z. Zhao, S. R. Bowman, and T. Linzen. (2021), “NOPE: A Corpus of Naturally-Occurring Presuppositions in English”, CoNLL 2021

K. Mulligan, R. Frank, and T. Linzen. (2021), “Structure Here, Bias There: Hierarchical Generalization by Jointly Learning Syntactic Transformations”, Proceedings of the Society for Computation in Linguistics, Vol. 4, pages 125-135

L. Aina, and T. Linzen. (2021), “The Language Model Understood the Prompt was Ambiguous: Probing Syntactic Uncertainty Through Generation”, BlackboxNLP 2021

W. Zhu, P. Shaw, T. Linzen, and F. Sha. (2021), “Learning to Generalize Compositionally by Transferring Across Semantic Parsing Tasks”, arXiv preprint arXiv:2111.05013

U. Arora, W. Huang, and H. He. (2021), “Types of out-of-distribution texts and how to detect them”, EMNLP, pages 10687-10701

Y. Chen, R. Zhong, S. Zha, G. Karypis, and H. He. (2021), “Meta-learning via language model in-context tuning”, ACL, pages 719-730

F. Ladhak, E. Durmus, H. He, C. Cardie, and K. McKeown. (2021), “Faithful or extractive? on mitigating the faithfulness-abstractiveness trade-off in abstractive summarization”, ACL, pages 1410-1421

N. Joshi, and H. He. (2021), “An investigation of the (in) effectiveness of counterfactually augmented data”, ACL, pages 3668-3681

V. Padmakumar, and H. He. (2021), “Unsupervised extractive summarization using pointwise mutual information”, EACL, pages 2505-2512

Y. Dranker, H. He, and Y. Belinkov. (2021), “IRM—when it works and when it doesn’t: A test case of natural language inference”, Advances in Neural Information Processing Systems, Vol. 34, pages 18212-18224

V. Padmakumar, and H. He. (2021), “Machine-in-the-loop rewriting for creative image captioning”, NAACL-HLT, pages 573-586

K. Chang, H. He, R. Jia, and S. Singh. (2021), “Robustness and adversarial examples in natural language processing”, Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts, pages 22-26

R. Y. Pang, H. He, and K. Cho. (2021), “Amortized noisy channel neural machine translation”, arXiv preprint arXiv:2112.08670

B. Chen, A. Rouditchenko, K. Duarte, H. Kuehne, S. Thomas, A. Boggust, R. Panda, B. Kingsbury, R. Feris, D. Harwath, J. Glass, M. Picheny, and S. Chang. (2021), “Multimodal clustering networks for self-supervised learning from unlabeled videos”, Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 8012-8021

J. Li, V. Manohar, P. Chitkara, A. Tjandra, M. Picheny, F. Zhang, X. Zhang, and Y. Saraf. (2021), “Accent-robust automatic speech recognition using supervised and unsupervised wav2vec embeddings”, arXiv preprint arXiv:2110.03520

A. Rouditchenko, A. Boggust, D. Harwath, S. Thomas, H. Kuehne, B. Chen, R. Panda, R. Feris, B. Kingsbury, M. Picheny, and J. Glass. (2021), “Cascaded multilingual audio-visual learning from videos”, Interspeech, pages 3006-3010

S. Cha, W. Hou, H. Jung, M. Phung, M. Picheny, H. Kuo, S. Thomas, and E. Morais. (2021), “Speak or chat with me: End-to-end spoken language understanding system with flexible inputs”, Interspeech, pages 4723-4727

J. Tumminia, A. Kuznecov, S. Tsilerides, I. Weinstein, B. McFee, M. Picheny, and A. R. Kaufman. (2021), “Diarization of legal proceedings. identifying and transcribing judicial speech from recorded court audio”, arXiv preprint arXiv:2104.01304

X. Chu, E. Combs, A. Wang, and M. Picheny. (2021), “Accented Speech Recognition Inspired by Human Perception”, arXiv preprint arXiv:2104.04627

P. Izmailov, S. Vikram, M. D. Hoffman, and A. G. Wilson. (2021), “What Are Bayesian Neural Network Posteriors Really Like?”, International Conference on Machine Learning

M. Finzi, M. Welling, and A. G. Wilson. (2021), “A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups”, International Conference on Machine Learning

S. Stanton, P. Izmailov, P. Kirichenko, A. A. Alemi, and A. G. Wilson. (2021), “Does knowledge distillation really work?”, Advances in Neural Information Processing Systems, Vol. 34, pages 6906-6919

B. Amos, S. Stanton, D. Yarats, and A. G. Wilson. (2021), “On the model-based stochastic value gradient for continuous reinforcement learning”, Learning for Dynamics and Control, pages 6-20

G. W. Benton, W. J. Maddox, S. Lotfi, and A. G. Wilson. (2021), “Loss Surface Simplexes for Mode Connecting Volumes and Fast Ensembling”, International Conference on Machine Learning

W. J. Maddox, M. Balandat, A. G. Wilson, and E. Bakshy. (2021), “Bayesian optimization with high-dimensional outputs”, Advances in neural information processing systems, Vol. 34, pages 19274-19287

P. Izmailov, P. Nicholson, S. Lotfi, and A. G. Wilson. (2021), “Dangers of Bayesian model averaging under covariate shift”, Advances in Neural Information Processing Systems, Vol. 34, pages 3309-3322

W. Maddox, S. Tang, P. Moreno, A. G. Wilson, and A. Damianou. (2021), “Fast adaptation with linearized neural networks”, International Conference on Artificial Intelligence and Statistics, pages 2737-2745

M. Finzi, G. Benton, and A. G. Wilson. (2021), “Residual pathway priors for soft equivariance constraints”, Advances in Neural Information Processing Systems, Vol. 34, pages 30037-30049

S. Stanton, W. Maddox, I. Delbridge, and A. G. Wilson. (2021), “Kernel interpolation for scalable online Gaussian processes”, International Conference on Artificial Intelligence and Statistics, pages 3133-3141

W. J. Maddox, S. Stanton, and A. G. Wilson. (2021), “Conditioning sparse variational gaussian processes for online decision-making”, Advances in Neural Information Processing Systems, Vol. 34, pages 6365-6379

P. Kirichenko, M. Farajtabar, D. Rao, B. Lakshminarayanan, N. Levine, A. Li, H. Hu, A. G. Wilson, and R. Pascanu. (2021), “Task-agnostic continual learning with hybrid probabilistic models”, arXiv preprint arXiv:2106.12772

S. Kapoor, M. Finzi, K. A. Wang, and A. G. Wilson. (2021), “SKIing on Simplices: Kernel Interpolation on the Permutohedral Lattice for Scalable Gaussian Processes”, International Conference on Machine Learning

N. Gruver, S. Stanton, P. Kirichenko, M. Finzi, P. Maffettone, V. Myers, E. Delaney, P. Greenside, and A. G. Wilson. (2021), “Effective surrogate models for protein design with bayesian optimization”, ICML Workshop on Computational Biology

S. Sun, J. Shi, A. G. Wilson, and R. Grosse. (2021), “Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition”, International Conference on Machine Learning

W. J. Maddox, S. Kapoor, and A. G. Wilson. (2021), “When are Iterative Gaussian Processes Reliably Accurate?”, arXiv preprint arXiv:2112.15246

D. Brandfonbrener, W. Whitney, R. Ranganath, and J. Bruna. (2021), “Offline rl without off-policy evaluation”, Advances in neural information processing systems, Vol. 34, pages 4933-4946

L. Zhang, M. Goldstein, and R. Ranganath. (2021), “Understanding failures in out-of-distribution detection with deep generative models”, International Conference on Machine Learning, pages 12427-12436

N. Jethani, M. Sudarshan, I. C. Covert, S. Lee, and R. Ranganath. (2021), “Fastshap: Real-time shapley value estimation”, International Conference on Learning Representations

N. Jethani, M. Sudarshan, Y. Aphinyanaphongs, and R. Ranganath. (2021), “Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their Interpretations.”, International Conference on Artificial Intelligence and Statistics, pages 1459-1467

A. Puli, L. H. Zhang, E. K. Oermann, and R. Ranganath. (2021), “Out-of-distribution generalization in the presence of nuisance-induced spurious correlations”, ICLR

D. Brandfonbrener, W. Whitney, R. Ranganath, and J. Bruna. (2021), “Offline contextual bandits with overparameterized models”, International Conference on Machine Learning, pages 1049-1058

X. Han, M. Goldstein, A. Puli, T. Wies, A. Perotte, and R. Ranganath. (2021), “Inverse-weighted survival games”, Advances in neural information processing systems, Vol. 34, pages 2160-2172

J. Altosaar, R. Ranganath, and W. Tansey. (2021), “RankFromSets: Scalable set recommendation with optimal recall”, Stat, Vol. 10, pages e363

M. Sudarshan, A. Puli, L. Subramanian, S. Sankararaman, and R. Ranganath. (2021), “Contra: Contrarian statistics for controlled variable selection”, International conference on artificial intelligence and statistics, pages 1900-1908

D. Brandfonbrener, W. F. Whitney, R. Ranganath, and J. Bruna. (2021), “Quantile filtered imitation learning”, arXiv preprint arXiv:2112.00950

W. V. Amsterdam, J. Verhoeff, N. Harlianto, G. Bartholomeus, A. M. Puli, P. D. Jong, T. Leiner, A. V. Lindert, R. Eijkemans, and R. Ranganath. (2021), “PD-0843 Individual treatment effect estimation in the presence of unobserved confounding for lung cancer”, Radiotherapy and Oncology, Vol. 161, pages S677-S678

A. Katz, X. Han, N. Jethani, C. Barbhaiya, S. Bernstein, D. Holmes, R. Knotts, A. Kushnir, D. Park, M. Spinelli, R. Ranganath, L. Chinitz, and L. Jankelson. (2021), “B-PO05-162 DEVELOPMENT OF A QRS-AGNOSTIC QT CORRECTION METHOD USING DEEP LEARNING”, Heart Rhythm, Vol. 18, pages S438

N. Jethani, H. Zhang, L. A. Chinitz, Y. Aphinyanaphongs, R. Ranganath, and L. Jankelson. (2021), “B-PO03-175 QTNET: PREDICTING DRUG-INDUCED QT PROLONGATION WITH DEEP NEURAL NETWORKS”, Heart Rhythm, Vol. 18, pages S260-S261

N. Jethani, H. Zhang, L. Chinitz, Y. Aphinyanaphongs, R. Ranganath, and L. Jankelson. (2021), “QTNet: Predicting Drug-Induced QT prolongation with Deep Neural Networks”, medRxiv, pages 2021.03. 24.21254235

S. C. Mirabal, T. Chkhikvadze, P. Theprungsirikul, L. Roca-Nelson, B. Yu, R. Ranganath, C. Fernandez-Granda, S. E. Saith, and R. Jervis. (2021), “CLLNICAL PRESENTATION AND OUTCOMES OF MORTALITY IN HISPANIC PATIENTS HOSPITALIZED WITH 2019 NOVEL CORONAVIRUS IN NEW YORK CITY”, Journal of General Internal Medicine, pages S58-S59

M. Nye, M. Tessler, J. Tenenbaum, and B. M. Lake. (2021), “Improving coherence and consistency in neural sequence models with dual-system, neuro-symbolic reasoning”, Advances in Neural Information Processing Systems, Vol. 34, pages 25192-25204

R. Vedantam, A. Szlam, M. Nickel, A. Morcos, and B. M. Lake. (2021), “CURI: A benchmark for productive concept learning under uncertainty”, International Conference on Machine Learning, pages 10519-10529

K. Gandhi, G. Stojnic, B. M. Lake, and M. R. Dillon. (2021), “Baby Intuitions Benchmark (BIB): Discerning the goals, preferences, and actions of others”, Advances in neural information processing systems, Vol. 34, pages 9963-9976

R. Feinman, and B. M. Lake. (2021), “Learning task-general representations with generative neuro-symbolic modeling”, International Conference on Learning Representations (ICLR)

A. Johnson, W. K. Vong, B. M. Lake, and T. M. Gureckis. (2021), “Fast and flexible: Human program induction in abstract reasoning tasks”, Proceedings of the 43rd Annual Conference of the Cognitive Science Society

Y. Zhou, and B. M. Lake. (2021), “Flexible Compositional Learning of Structured Visual Concepts”, Proceedings of the 43rd Annual Conference of the Cognitive Science Society

W. K. Vong, E. Orhan, and B. Lake. (2021), “Cross-situational word learning from naturalistic headcam data”, 34th CUNY Conference on Human Sentence Processing

G. Davidson, and B. Lake. (2021), “Examining Infant Relation Categorization Through Deep Neural Networks”, Proceedings of the Annual Meeting of the Cognitive Science Society, Vol. 43

E. Kosoy, M. Belyi, C. Snell, B. Lake, J. Tenenbaum, and A. Gopnik. (2021), “The Omniglot Jr. challenge; Can a model achieve child-level character generation and classification?”, Proceedings of the Annual Meeting of the Cognitive Science Society, Vol. 43

G. Stojnic, K. Gandhi, B. Lake, and M. R. Dillon. (2021), “Evaluating infants’ reasoning about agents using the Baby Intuitions Benchmark (BIB)”, Proceedings of the Annual Meeting of the Cognitive Science Society, Vol. 43

A. R. Tartaglini, W. K. Vong, and B. Lake. (2021), “Modeling artificial category learning from pixels: Revisiting Shepard, Hovland, and Jenkins (1961) with deep neural networks”, Proceedings of the Annual Meeting of the Cognitive Science Society, Vol. 43

S. R. Bowman, and G. E. Dahl. (2021), “What Will it Take to Fix Benchmarking in Natural Language Understanding?”, Proceedings of NAACL

N. Nangia, S. Sugawara, H. Trivedi, A. Warstadt, C. Vania, and S. R. Bowman. (2021), “What Ingredients Make for an Effective Crowdsourcing Protocol for Difficult NLU Data Collection Tasks?”, Proceedings of ACL

C. Vania, P. M. Htut, W. Huang, D. Mungra, R. Y. Pang, J. Phang, H. Liu, K. Cho, and S. R. Bowman. (2021), “Comparing Test Sets with Item Response Theory”, Proceedings of ACL

A. Parrish, S. Schuster, A. Warstadt, O. Agha, S. Lee, Z. Zhao, S. R. Bowman, and T. Linzen. (2021), “NOPE: A corpus of naturally-occurring presuppositions in English”, Proceedings of CoNLL

D. Chen, Z. Yu, and S. R. Bowman. (2021), “Clean or annotate: How to spend a limited data collection budget”, arXiv preprint arXiv:2110.08355

J. Phang, A. Chen, W. Huang, and S. R. Bowman. (2021), “Adversarially constructed evaluation sets are more challenging, but may not be fair”, arXiv preprint arXiv:2111.08181

J. Phang, H. Liu, and S. R. Bowman. (2021), “Fine-Tuned Transformers Show Clusters of Similar Representations Across Layers”, Proceedings of BlackBoxNLP

A. Suhr, C. Vania, N. Nangia, M. Sap, M. Yatskar, S. Bowman, and Y. Artzi. (2021), “Crowdsourcing Beyond Annotation: Case Studies in Benchmark Data Collection”, EMNLP 2021 Tutorial, pages 1-6

J. Pfeiffer, A. Kamath, A. Rücklé, K. Cho, and I. Gurevych. (2021), “AdapterFusion: Non-destructive task composition for transfer learning”, EACL

V. Gligorijević, P. D. Renfrew, T. Kosciolek, J. K. Leman, D. Berenberg, T. Vatanen, C. Chandler, B. C. Taylor, I. M. Fisk, H. Vlamakis, R. J. Xavier, R. Knight, K. Cho, and R. Bonneau. (2021), “Structure-based protein function prediction using graph convolutional networks”, Nature communications, Vol. 12, pages 3168

E. Perez, D. Kiela, and K. Cho. (2021), “True few-shot learning with language models”, Advances in neural information processing systems, Vol. 34, pages 11054-11070

S. Park, J. Moon, S. Kim, W. I. Cho, J. Han, J. Park, C. Song, J. Kim, Y. Song, T. Oh, J. Lee, J. Oh, S. Lyu, Y. Jeong, I. Lee, S. Seo, D. Lee, H. Kim, M. Lee, S. Jang, S. Do, S. Kim, K. Lim, J. Lee, K. Park, J. Shin, S. Kim, L. Park, A. Oh, J. Ha, and K. Cho. (2021), “KLUE: Korean language understanding evaluation”, NeurIPS Datasets and Benchmarks

Y. Shen, N. Wu, J. Phang, J. Park, K. Liu, S. Tyagi, L. Heacock, S. G. Kim, L. Moy, K. Cho, and K. J. Geras. (2021), “An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization”, Medical image analysis, Vol. 68, pages 101908

I. Drori, Y. Krishnamurthy, R. Rampin, R. Lourenco, J. P. Ono, K. Cho, C. Silva, and J. Freire. (2021), “AlphaD3M: Machine learning pipeline synthesis”, arXiv preprint arXiv:2111.02508

O. Mahmood, E. Mansimov, R. Bonneau, and K. Cho. (2021), “Masked graph modeling for molecule generation”, Nature communications, Vol. 12, pages 3156

S. Jastrzebski, D. Arpit, O. Astrand, G. B. Kerg, H. Wang, C. Xiong, R. Socher, K. Cho, and K. J. Geras. (2021), “Catastrophic fisher explosion: Early phase fisher matrix impacts generalization”, International Conference on Machine Learning, pages 4772-4784

H. Wang, W. Li, X. Jin, K. Cho, H. Ji, J. Han, and M. D. Burke. (2021), “Chemical-reaction-aware molecule representation learning”, ICLR

T. He, J. Liu, K. Cho, M. Ott, B. Liu, J. Glass, and F. Peng. (2021), “Analyzing the forgetting problem in pretrain-finetuning of open-domain dialogue response models”, Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 1121-1133

S. Welleck, J. Liu, R. L. Bras, H. Hajishirzi, Y. Choi, and K. Cho. (2021), “Naturalproofs: Mathematical theorem proving in natural language”, NeurIPS Datasets and Benchmarks

A. Tjärnberg, O. Mahmood, C. A. Jackson, G. Saldi, K. Cho, L. A. Christiaen, and R. A. Bonneau. (2021), “Optimal tuning of weighted kNN-and diffusion-based methods for denoising single cell genomics data”, PLoS computational biology, Vol. 17, pages e1008569

V. Gligorijević, D. Berenberg, S. Ra, A. Watkins, S. Kelow, K. Cho, and R. Bonneau. (2021), “Function-guided protein design by deep manifold sampling”, bioRxiv, pages 2021.12. 22.473759

M. Li, S. Li, Z. Wang, L. Huang, K. Cho, H. Ji, J. Han, and C. Voss. (2021), “The future is not one-dimensional: Complex event schema induction by graph modeling for event prediction”, EMNLP, pages 5203-5215

C. Vania, P. M. Htut, W. Huang, D. Mungra, R. Y. Pang, J. Phang, H. Liu, K. Cho, and S. R. Bowman. (2021), “Comparing test sets with item response theory”, ACL/IJCNLP, pages 1141-1158

E. Perez, D. Kiela, and K. Cho. (2021), “Rissanen data analysis: Examining dataset characteristics via description length”, International Conference on Machine Learning, pages 8500-8513

M. Li, S. Li, Z. Wang, L. Huang, K. Cho, H. Ji, J. Han, and C. Voss. (2021), “Future is not one-dimensional: Graph modeling based complex event schema induction for event prediction”, arXiv preprint arXiv:2104.06344, Vol. 2

M. Barot, V. Gligorijević, K. Cho, and R. Bonneau. (2021), “NetQuilt: deep multispecies network-based protein function prediction using homology-informed network similarity”, Bioinformatics, Vol. 37, pages 2414-2422

W. F. Whitney, M. Bloesch, J. T. Springenberg, A. Abdolmaleki, K. Cho, and M. Riedmiller. (2021), “Decoupled exploration and exploitation policies for sample-efficient reinforcement learning”, arXiv preprint arXiv:2101.09458

J. Hu, H. Hayashi, K. Cho, and G. Neubig. (2021), “DEEP: DEnoising entity pre-training for neural machine translation”, ACL, pages 1753-1766

B. Stadnick, J. Witowski, V. Rajiv, J. Chłędowski, F. E. Shamout, K. Cho, and K. J. Geras. (2021), “Meta-repository of screening mammography classifiers”, arXiv preprint arXiv:2108.04800

D. J. Im, C. Savin, and K. Cho. (2021), “Online hyperparameter optimization by real-time recurrent learning”, arXiv e-prints, pages arXiv: 2102.07813

S. Welleck, and K. Cho. (2021), “MLE-guided parameter search for task loss minimization in neural sequence modeling”, AAAI

Y. Kim, S. Jeong, and K. Cho. (2021), “Linda: Unsupervised learning to interpolate in natural language processing”, arXiv preprint arXiv:2112.13969

N. Wu, Z. Huang, Y. Shen, J. Park, J. Phang, T. Makino, S. G. Kim, K. Cho, L. Heacock, L. Moy, and K. J. Geras. (2021), “Reducing false-positive biopsies using deep neural networks that utilize both local and global image context of screening mammograms”, Journal of Digital Imaging, Vol. 34, pages 1414-1423

D. J. Im, K. Cho, and N. Razavian. (2021), “Causal effect variational autoencoder with uniform treatment”, arXiv preprint arXiv:2111.08656

H. Han, S. Ahn, Y. Choi, I. Chung, S. Kim, and K. Cho. (2021), “Monotonic simultaneous translation with chunk-wise reordering and refinement”, WMT@EMNLP, pages 1110-1123

T. He, K. Cho, and J. Glass. (2021), “An empirical study on few-shot knowledge probing for pretrained language models”, arXiv preprint arXiv:2109.02772

C. Resnick, O. Litany, A. Kar, K. Kreis, J. Lucas, K. Cho, and S. Fidler. (2021), “Causal bert: Improving object detection by searching for challenging groups”, Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2972-2981

I. Kulikov, M. Eremeev, and K. Cho. (2021), “Characterizing and addressing the issue of oversmoothing in neural autoregressive sequence modeling”, AACL/IJCNLP, pages 1115-1124

R. Y. Pang, H. He, and K. Cho. (2021), “Amortized noisy channel neural machine translation”, arXiv preprint arXiv:2112.08670

W. Falcon, A. H. Jha, T. Koker, and K. Cho. (2021), “AASAE: Augmentation-Augmented Stochastic Autoencoders”, arXiv preprint arXiv:2107.12329

I. Kulikov, S. Welleck, and K. Cho. (2021), “Mode recovery in neural autoregressive sequence modeling”, SPNLP@ACL-IJCNLP, pages 44-52

R. Y. Pang, H. He, and K. Cho. (2021), “Amortized Noisy Channel Neural Machine Translation”, arXiv e-prints, pages arXiv: 2112.08670

A. Brundyn, J. Swanson, K. Cho, D. Kondziolka, and E. Oermann. (2021), “Stereo Video Reconstruction Without Explicit Depth Maps for Endoscopic Surgery”, arXiv preprint arXiv:2109.08227

C. Resnick, O. Litany, C. Heiß, H. Larochelle, J. Bruna, and K. Cho. (2021), “Self-Supervised Equivariant Scene Synthesis from Video”, arXiv preprint arXiv:2102.00863

A. Rives, J. Meier, T. Sercu, S. Goyal, Z. Lin, J. Liu, D. Guo, M. Ott, C. L. Zitnick, J. Ma, and R. Fergus. (2021), “Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences”, Proceedings of the National Academy of Sciences, Vol. 118, pages e2016239118

D. Yarats, A. Zhang, I. Kostrikov, B. Amos, J. Pineau, and R. Fergus. (2021), “Improving sample efficiency in model-free reinforcement learning from images”, Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35, pages 10674-10681

I. Kostrikov, R. Fergus, J. Tompson, and O. Nachum. (2021), “Offline reinforcement learning with fisher divergence critic regularization”, International Conference on Machine Learning, pages 5774-5783

D. Yarats, R. Fergus, A. Lazaric, and L. Pinto. (2021), “Mastering visual continuous control: Improved data-augmented reinforcement learning”, ICLR

D. Yarats, R. Fergus, A. Lazaric, and L. Pinto. (2021), “Reinforcement learning with prototypical representations”, International Conference on Machine Learning, pages 11920-11931

R. Raileanu, and R. Fergus. (2021), “Decoupling value and policy for generalization in reinforcement learning”, International Conference on Machine Learning, pages 8787-8798

R. Raileanu, M. Goldstein, D. Yarats, I. Kostrikov, and R. Fergus. (2021), “Automatic data augmentation for generalization in reinforcement learning”, Advances in Neural Information Processing Systems, Vol. 34, pages 5402-5415

A. Jaegle, Y. Sulsky, A. Ahuja, J. Bruce, R. Fergus, and G. Wayne. (2021), “Imitation by predicting observations”, International Conference on Machine Learning, pages 4665-4676

M. M. Bronstein, J. Bruna, T. Cohen, and P. Veličković. (2021), “Geometric deep learning: Grids, groups, graphs, geodesics, and gauges”, arXiv preprint arXiv:2104.13478

D. Brandfonbrener, W. Whitney, R. Ranganath, and J. Bruna. (2021), “Offline rl without off-policy evaluation”, Advances in neural information processing systems, Vol. 34, pages 4933-4946

A. Bietti, L. Venturi, and J. Bruna. (2021), “On the sample complexity of learning under geometric stability”, Advances in neural information processing systems, Vol. 34, pages 18673-18684

F. Williams, M. Trager, J. Bruna, and D. Zorin. (2021), “Neural splines: Fitting 3d surfaces with infinitely-wide neural networks”, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9949-9958

J. Rahme, S. Jelassi, J. Bruna, and S. M. Weinberg. (2021), “A permutation-equivariant neural network architecture for auction design”, Proceedings of the AAAI conference on artificial intelligence, Vol. 35, pages 5664-5672

M. J. Song, I. Zadik, and J. Bruna. (2021), “On the cryptographic hardness of learning single periodic neurons”, Advances in neural information processing systems, Vol. 34, pages 29602-29615

A. Zweig, and J. Bruna. (2021), “A functional perspective on learning symmetric functions with neural networks”, International Conference on Machine Learning, pages 13023-13032

K. Otness, A. Gjoka, J. Bruna, D. Panozzo, B. Peherstorfer, T. Schneider, and D. Zorin. (2021), “An extensible benchmark suite for learning to simulate physical systems”, NeurIPS Datasets and Benchmarks

D. Brandfonbrener, W. Whitney, R. Ranganath, and J. Bruna. (2021), “Offline contextual bandits with overparameterized models”, International Conference on Machine Learning, pages 1049-1058

C. Domingo-Enrich, A. Bietti, E. Vanden-Eijnden, and J. Bruna. (2021), “On energy-based models with overparametrized shallow neural networks”, International Conference on Machine Learning, pages 2771-2782

Y. Arjevani, J. Bruna, M. Field, J. Kileel, M. Trager, and F. Williams. (2021), “Symmetry breaking in symmetric tensor decomposition”, arXiv preprint arXiv:2103.06234

D. Brandfonbrener, W. F. Whitney, R. Ranganath, and J. Bruna. (2021), “Quantile filtered imitation learning”, arXiv preprint arXiv:2112.00950

C. Domingo-Enrich, A. Bietti, M. Gabrié, J. Bruna, and E. Vanden-Eijnden. (2021), “Dual training of energy-based models with overparametrized shallow neural networks”, arXiv preprint arXiv:2107.05134

J. Bruna. (2021), “Aprenentatge algorísmic i xarxes neuronals profundes”, Butlletí de la Societat Catalana de Matemàtiques, Vol. 36, pages 5-67

L. Chen, Z. Chen, and J. Bruna. (2021), “Learning the relevant substructures for tasks on graph data”, ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 8528-8532

Y. He, and J. Bruna. (2021), “Multi-fidelity Stability for Graph Representation Learning”, arXiv preprint arXiv:2111.12865

C. Resnick, O. Litany, C. Heiß, H. Larochelle, J. Bruna, and K. Cho. (2021), “Self-Supervised Equivariant Scene Synthesis from Video”, arXiv preprint arXiv:2102.00863

J. Zbontar, L. Jing, I. Misra, Y. LeCun, and S. Deny. (2021), “Barlow twins: Self-supervised learning via redundancy reduction”, International Conference on Machine Learning (ICML), Vol. 139, pages 12310-12320

A. Bardes, J. Ponce, and Y. LeCun. (2021), “Vicreg: Variance-invariance-covariance regularization for self-supervised learning”, ICLR

A. Kamath, M. Singh, Y. LeCun, G. Synnaeve, I. Misra, and N. Carion. (2021), “Mdetr-modulated detection for end-to-end multi-modal understanding”, Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1780-1790

Y. Bengio, Y. Lecun, and G. Hinton. (2021), “Deep learning for AI”, Communications of the ACM, Vol. 64, pages 58-65

L. Jing, P. Vincent, Y. LeCun, and Y. Tian. (2021), “Understanding dimensional collapse in contrastive self-supervised learning”, ICLR

R. Balestriero, J. Pesenti, and Y. LeCun. (2021), “Learning in high dimension always amounts to extrapolation”, arXiv preprint arXiv:2110.09485

Z. Yun, Y. Chen, B. A. Olshausen, and Y. LeCun. (2021), “Transformer visualization via dictionary learning: contextualized embedding as a linear superposition of transformer factors”, DeeLIO@NAACL-HLT, pages 1-10

Y. LeCun, and I. Misra. (2021), “Self-supervised learning: The dark matter of intelligence, 2021”, URL https://ai. facebook. com/blog/self-supervised-learning-the-dark-matter-of-intelligence, Vol. 95, pages 96

B. Rozière, M. Riviere, O. Teytaud, J. Rapin, Y. LeCun, and C. Couprie. (2021), “Inspirational adversarial image generation”, IEEE Transactions on Image Processing, Vol. 30, pages 4036-4045

Z. Lin, T. Sercu, Y. LeCun, and A. Rives. (2021), “Deep generative models create new and diverse protein structures”, Machine Learning for Structural Biology Workshop, NeurIPS

T. Sercu, R. Verkuil, J. Meier, B. Amos, Z. Lin, C. Chen, J. Liu, Y. LeCun, and A. Rives. (2021), “Neural potts model”, bioRxiv, pages 2021.04. 08.439084

K. Evtimova, and Y. LeCun. (2021), “Sparse coding with multi-layer decoders using variance regularization”, Trans. Mach. Learn. Res.

2020

R. M. Jones, A. Sharma, R. Hotchkiss, J. W. Sperling, J. Hamburger, C. Ledig, R. O’Toole, M. Gardner, S. Venkatesh, M. M. Roberts, R. Sauvestre, M. Shatkhin, A. Gupta, S. Chopra, M. Kumaravel, A. Daluiski, W. Plogger, J. Nascone, H. G. Potter, and R. V. Lindsey. (2020), “Assessment of a deep-learning system for fracture detection in musculoskeletal radiographs”, NPJ digital medicine, Vol. 3, pages 144

M. Laskin, K. Lee, A. Stooke, L. Pinto, P. Abbeel, and A. Srinivas. (2020), “Reinforcement learning with augmented data”, Advances in neural information processing systems, Vol. 33, pages 19884-19895

N. Hansen, R. Jangir, Y. Sun, G. Alenyà, P. Abbeel, A. A. Efros, L. Pinto, and X. Wang. (2020), “Self-supervised policy adaptation during deployment”, ICLR

Y. Zhang, P. Abbeel, and L. Pinto. (2020), “Automatic curriculum learning through value disagreement”, Advances in Neural Information Processing Systems, Vol. 33, pages 7648-7659

A. Li, L. Pinto, and P. Abbeel. (2020), “Generalized hindsight for reinforcement learning”, Advances in neural information processing systems, Vol. 33, pages 7754-7767

Q. Zhang, T. Xiao, A. A. Efros, L. Pinto, and X. Wang. (2020), “Learning cross-domain correspondence for control with dynamics cycle-consistency”, ICLR

B. Chen, A. Sax, G. Lewis, I. Armeni, S. Savarese, A. Zamir, J. Malik, and L. Pinto. (2020), “Robust policies via mid-level visual representations: An experimental study in manipulation and navigation”, CoRL, pages 2328-2346

D. Gandhi, A. Gupta, and L. Pinto. (2020), “Swoosh! Rattle! Thump!-Actions that Sound”, Robotics: Science and Systems, 2020

D. Hejna, L. Pinto, and P. Abbeel. (2020), “Hierarchically decoupled imitation for morphological transfer”, International Conference on Machine Learning, pages 4159-4171

N. Kim, and T. Linzen. (2020), “COGS: A Compositional Generalization Challenge Based on Semantic Interpretation”, EMNLP

T. Linzen. (2020), “How Can We Accelerate Progress Towards Human-like Linguistic Generalization?”, Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL)

J. Min, R. T. McCoy, D. Das, E. Pitler, and T. Linzen. (2020), “Syntactic Data Augmentation Increases Robustness to Inference Heuristics”, Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL)

R. T. McCoy, R. Frank, and T. Linzen. (2020), “Does syntax need to grow on trees? Sources of hierarchical inductive bias in sequence-to-sequence networks”, Transactions of the Association for Computational Linguistics, Vol. 8, pages 125–140

A. Mueller, G. Nicolai, P. Petrou-Zeniou, N. Talmina, and T. Linzen. (2020), “Cross-Linguistic Syntactic Evaluation of Word Prediction Models”, Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL)

R. T. McCoy, E. Grant, P. Smolensky, T. L. Griffiths, and T. Linzen. (2020), “Universal linguistic inductive biases via meta-learning”, Proceedings of the 42nd Annual Conference of the Cognitive Science Society, pages 737–743

S. Arehalli, and T. Linzen. (2020), “Neural Language Models Capture Some, But Not All, Agreement Attraction Effects”, Proceedings of the 42nd Annual Conference of the Cognitive Science Society, pages 370–376

M. A. Lepori, T. Linzen, and R. T. McCoy. (2020), “Representations of Syntax [MASK] Useful: Effects of Constituency and Dependency Structure in Recursive LSTMs”, Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL)

N. Havron, C. Scaff, M. J. Carbajal, T. Linzen, A. Barrault, and A. Christophe. (2020), “Priming syntactic ambiguity resolution in children and adults”, Language, Cognition and Neuroscience, Vol. 35, pages 1445-1455

R. T. McCoy, T. Linzen, E. Dunbar, and P. Smolensky. (2020), “Tensor Product Decomposition Networks: Uncovering Representations of Structure Learned by Neural Networks”, Proceedings of the Society for Computation in Linguistics 2020, pages 233-234

R. Fernández, and T. Linzen. (2020), “Proceedings of the 24th Conference on Computational Natural Language Learning”, Proceedings of the 24th Conference on Computational Natural Language Learning

N. Talmina, and T. Linzen. (2020), “Neural network learning of the Russian genitive of negation: optionality and structure sensitivity”, Proceedings of the Society for Computation in Linguistics 2020, pages 42-50

E. Durmus, H. He, and M. Diab. (2020), “FEQA: A question answering evaluation framework for faithfulness assessment in abstractive summarization”, ACL, pages 5055-5070

J. Guo, H. He, T. He, L. Lausen, M. Li, H. Lin, X. Shi, C. Wang, J. Xie, S. Zha, A. Zhang, H. Zhang, Z. Zhang, Z. Zhang, S. Zheng, and Y. Zhu. (2020), “Gluoncv and gluonnlp: Deep learning in computer vision and natural language processing”, The Journal of Machine Learning Research, Vol. 21, pages 845-851

L. Tu, G. Lalwani, S. Gella, and H. He. (2020), “An empirical study on robustness to spurious correlations using pre-trained language models”, Transactions of the Association for Computational Linguistics, Vol. 8, pages 621-633

R. Y. Pang, and H. He. (2020), “Text generation by learning from demonstrations”, ICLR

A. Rouditchenko, A. Boggust, D. Harwath, B. Chen, D. Joshi, S. Thomas, K. Audhkhasi, H. Kuehne, R. Panda, R. Feris, B. Kingsbury, M. Picheny, A. Torralba, and J. Glass. (2020), “Avlnet: Learning audio-visual language representations from instructional videos”, Interspeech, pages 1584-1588

Y. Huang, H. Kuo, S. Thomas, Z. Kons, K. Audhkhasi, B. Kingsbury, R. Hoory, and M. Picheny. (2020), “Leveraging unpaired text data for training end-to-end speech-to-intent systems”, ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 7984-7988

X. Cui, W. Zhang, U. Finkler, G. Saon, M. Picheny, and D. Kung. (2020), “Distributed training of deep neural network acoustic models for automatic speech recognition: a comparison of current training strategies”, IEEE Signal Processing Magazine, Vol. 37, pages 39-49

W. Zhang, X. Cui, A. Kayi, M. Liu, U. Finkler, B. Kingsbury, G. Saon, Y. Mroueh, A. Buyuktosunoglu, P. Das, D. Kung, and M. Picheny. (2020), “Improving efficiency in large-scale decentralized distributed training”, ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 3022-3026

M. Balandat, B. Karrer, D. Jiang, S. Daulton, B. Letham, A. G. Wilson, and E. Bakshy. (2020), “BoTorch: A framework for efficient Monte-Carlo Bayesian optimization”, Advances in neural information processing systems, Vol. 33, pages 21524-21538

A. G. Wilson, and P. Izmailov. (2020), “Bayesian deep learning and a probabilistic perspective of generalization”, Advances in Neural Information Processing Systems (NeurIPS)

M. Finzi, S. Stanton, P. Izmailov, and A. G. Wilson. (2020), “Generalizing convolutional neural networks for equivariance to lie groups on arbitrary continuous data”, International Conference on Machine Learning (ICML)

P. Kirichenko, P. Izmailov, and A. G. Wilson. (2020), “Why normalizing flows fail to detect out-of-distribution data”, Advances in Neural Information Processing Systems (NeurIPS)

P. Izmailov, W. J. Maddox, P. Kirichenko, T. Garipov, D. Vetrov, and A. G. Wilson. (2020), “Subspace inference for Bayesian deep learning”, Uncertainty in Artificial Intelligence, pages 1169-1179

G. Benton, M. Finzi, P. Izmailov, and A. G. Wilson. (2020), “Learning Invariances in Neural Networks”, Advances in Neural Information Processing Systems (NeurIPS)

A. G. Wilson. (2020), “The case for Bayesian deep learning”, arXiv preprint arXiv:2001.10995

M. Finzi, K. A. Wang, and A. G. Wilson. (2020), “Simplifying hamiltonian and lagrangian neural networks via explicit constraints”, Advances in Neural Information Processing Systems (NeurIPS)

W. J. Maddox, G. Benton, and A. G. Wilson. (2020), “Rethinking parameter counting in deep models: Effective dimensionality revisited”, arXiv preprint arXiv:2003.02139

Y. Wu, P. Zhou, A. G. Wilson, E. P. Xing, and Z. Hu. (2020), “Improving GAN Training with Probability Ratio Clipping and Sample Reweighting”, Advances in Neural Information Processing Systems (NeurIPS)

S. Lederer, Y. Choi, P. Ismailov, A. Wilson, S. Nadj-Perge, and E. Kim. (2020), “Unsupervised learning of nematic order from scanning tunneling spectroscopy on twisted bilayer graphene”, Bulletin of the American Physical Society, Vol. 65

X. Han, Y. Hu, L. Foschini, L. Chinitz, L. Jankelson, and R. Ranganath. (2020), “Deep learning models for electrocardiograms are susceptible to adversarial attack”, Nature medicine, Vol. 26, pages 360-363

N. Razavian, V. J. Major, M. Sudarshan, J. Burk-Rafel, P. Stella, H. Randhawa, S. Bilaloglu, J. Chen, V. Nguy, W. Wang, H. Zhang, I. Reinstein, D. Kudlowitz, C. Zenger, M. Cao, R. Zhang, S. Dogra, K. B. Harish, B. Bosworth, F. Francois, L. I. Horwitz, R. Ranganath, J. Austrian, and Y. Aphinyanaphongs. (2020), “A validated, real-time prediction model for favorable outcomes in hospitalized COVID-19 patients”, NPJ digital medicine, Vol. 3, pages 130

M. Goldstein, X. Han, A. Puli, A. Perotte, and R. Ranganath. (2020), “X-cal: Explicit calibration for survival analysis”, Advances in neural information processing systems, Vol. 33, pages 18296-18307

M. Sudarshan, W. Tansey, and R. Ranganath. (2020), “Deep direct likelihood knockoffs”, Advances in neural information processing systems, Vol. 33, pages 5036-5046

A. Puli, and R. Ranganath. (2020), “General control functions for causal effect estimation from ivs”, Advances in neural information processing systems, Vol. 33, pages 8440-8451

B. H. Foy, A. Li, J. P. McClung, R. Ranganath, and J. M. Higgins. (2020), “Data‐driven physiologic thresholds for iron deficiency associated with hematologic decline”, American Journal of Hematology, Vol. 95, pages 302-309

A. Puli, A. Perotte, and R. Ranganath. (2020), “Causal estimation with functional confounders”, Advances in neural information processing systems, Vol. 33, pages 5115-5125

A. J. Averitt, N. Vanitchanant, R. Ranganath, and A. J. Perotte. (2020), “The Counterfactual χ-GAN: Finding comparable cohorts in observational health data”, Journal of Biomedical Informatics, Vol. 109, pages 103515

A. J. Averitt, N. Vanitchanant, R. Ranganath, and A. J. Perotte. (2020), “The Counterfactual -GAN”, EMNLP, pages 5499-5512

S. A. Bhave, R. Ranganath, and A. Perotte. (2020), “Information theoretic approaches for testing missingness in predictive models”, ICML Workshop on the Art of Learning with Missing Values (Artemiss)

L. Ruis, J. Andreas, M. Baroni, D. Bouchacourt, and B. M. Lake. (2020), “A Benchmark for Systematic Generalization in Grounded Language Understanding”, Advances in Neural Information Processing Systems 33

M. I. Nye, A. Solar-Lezama, J. B. Tenenbaum, and B. M. Lake. (2020), “Learning Compositional Rules via Neural Program Synthesis”, Advances in Neural Information Processing Systems 33

A. E. Orhan, V. V. Gupta, and B. M. Lake. (2020), “Self-supervised learning through the eyes of a child”, Advances in Neural Information Processing Systems 33

M. Lewis, V. Cristiano, B. M. Lake, T. Kwan, and M. C. Frank. (2020), “The role of developmental change and linguistic experience in the mutual exclusivity effect”, Cognition, Vol. 198, pages 104191

B. M. Lake, and S. T. Piantadosi. (2020), “People infer recursive visual concepts from just a few examples”, Computational Brain & Behavior, Vol. 3, pages 54-65

K. Gandhi, and B. M. Lake. (2020), “Mutual exclusivity as a challenge for deep neural networks”, Advances in Neural Information Processing Systems 33

R. Feinman, and B. M. Lake. (2020), “Generating new concepts with hybrid neuro-symbolic models”, Proceedings of the 42nd Annual Conference of the Cognitive Science Society

W. K. Vong, and B. M. Lake. (2020), “Learning word-referent mappings and concepts from raw inputs”, CogSci

G. Davidson, and B. M. Lake. (2020), “Investigating simple object representations in model-free deep reinforcement learning”, Proceedings of the 42nd Annual Conference of the Cognitive Science Society

A. E. Orhan, V. V. Gupta, and B. M. Lake. (2020), “Self-supervised learning through the eyes of a child”, NeurIPS

W. K. Vong, and B. M. Lake. (2020), “Learning word-referent mappings and concepts from raw inputs”, CogSci

A. Jain, B. M. Lake, and T. M. Gureckis. (2020), “Extending the Rogers and McClelland Model of Semantic Cognition (2003) to work with Raw Pixel Information.”, CogSci

N. Nangia, C. Vania, R. Bhalerao, and S. R. Bowman. (2020), “CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models”, Proceedings of EMNLP

A. Warstadt, A. Parrish, H. Liu, A. Mohananey, W. Peng, S. Wang, and S. R. Bowman. (2020), “BLiMP: A benchmark of linguistic minimal pairs for english”, TACL

Y. Pruksachatkun, J. Phang, H. Liu, P. M. Htut, X. Zhang, R. Y. Pang, C. Vania, K. Kann, and S. R. Bowman. (2020), “Intermediate-Task Transfer Learning with Pretrained Models for Natural Language Understanding: When and Why Does It Work?”, Proceedings of ACL

Y. Zhang, A. Warstadt, H. Li, and S. R. Bowman. (2020), “When Do You Need Billions of Words of Pretraining Data?”, Proceedings of ACL

A. Warstadt, Y. Zhang, H. Li, H. Liu, and S. R. Bowman. (2020), “Learning Which Features Matter: RoBERTa Acquires a Preference for Linguistic Generalizations (Eventually)”, Proceedings of EMNLP

J. Phang, P. M. Htut, Y. Pruksachatkun, H. Liu, C. Vania, K. Kann, I. Calixto, and S. R. Bowman. (2020), “English Intermediate-Task Training Improves Zero-Shot Cross-Lingual Transfer Too”, Proceedings of AACL

A. Warstadt, and S. R. Bowman. (2020), “Can neural networks acquire a structural bias from raw linguistic data?”, Proceedings of CogSci

W. Huang, H. Liu, and S. R. Bowman. (2020), “Counterfactually-Augmented SNLI Training Data Does Not Yield Better Generalization Than Unaugmented Data”, Proceedings of the Workshop on Insights from Negative Results in NLP

S. R. Bowman, J. Palomaki, L. B. Soares, and E. Pitler. (2020), “New Protocols and Negative Results for Textual Entailment Data Collection”, Proceedings of EMNLP, pages 8203-8214

C. Vania, R. Chen, and S. R. Bowman. (2020), “Asking Crowdworkers to Write Entailment Examples: The Best of Bad Options”, Proceedings of AACL

H. Liu, W. Huang, D. A. Mungra, and S. R. Bowman. (2020), “Precise Task Formalization Matters in Winograd Schema Evaluations”, Proceedings of EMNLP

K. Kann, S. R. Bowman, and K. Cho. (2020), “Learning to learn morphological inflection for resource-poor languages”, Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34, pages 8058-8065

A. Mohananey, K. Kann, and S. R. Bowman. (2020), “Self-Training for Unsupervised Parsing with PRPN”, Proceedings of IWPT

J. Pfeiffer, A. Rücklé, C. Poth, A. Kamath, I. Vulić, S. Ruder, K. Cho, and I. Gurevych. (2020), “Adapterhub: A framework for adapting transformers”, EMNLP, pages 46-54

A. Wang, K. Cho, and M. Lewis. (2020), “Asking and Answering Questions to Evaluate the Factual Consistency of Summaries”, ACL

T. Schaffter, D. S. Buist, C. I. Lee, Y. Nikulin, D. Ribli, Y. Guan, W. Lotter, Z. Jie, H. Du, S. Wang, J. Feng, M. Feng, H. Kim, F. Albiol, A. Albiol, S. Morrell, Z. Wojna, M. E. Ahsen, U. Asif, A. J. Yepes, S. Yohanandan, S. Rabinovici-Cohen, D. Yi, B. Hoff, T. Yu, E. C. Neto, D. L. Rubin, P. Lindholm, L. R. Margolies, R. B. McBride, J. H. Rothstein, W. Sieh, R. Ben-Ari, S. Harrer, A. Trister, S. Friend, T. Norman, B. Sahiner, F. Strand, J. Guinney, G. Stolovitzky, L. Mackey, J. Cahoon, L. Shen, J. H. Sohn, H. Trivedi, Y. Shen, L. Buturovic, J. C. Pereira, J. S. Cardoso, E. Castro, K. T. Kalleberg, O. Pelka, I. Nedjar, K. J. Geras, F. Nensa, E. Goan, S. Koitka, L. Caballero, D. D. Cox, P. Krishnaswamy, G. Pandey, C. M. Friedrich, D. Perrin, C. Fookes, B. Shi, G. C. Negrie, M. Kawczynski, K. Cho, C. S. Khoo, J. Y. Lo, A. G. Sorensen, H. Jung, and D. D. Consortium. (2020), “Evaluation of combined artificial intelligence and radiologist assessment to interpret screening mammograms”, JAMA network open, Vol. 3, pages e200265-e200265

M. Li, S. Roller, I. Kulikov, S. Welleck, Y. Boureau, K. Cho, and J. Weston. (2020), “Don’t Say That! Making Inconsistent Dialogue Unlikely with Unlikelihood Training”, ACL

E. Perez, P. Lewis, W. Yih, K. Cho, and D. Kiela. (2020), “Unsupervised question decomposition for question answering”, EMNLP, pages 8864-8880

C. Wang, K. Cho, and J. Gu. (2020), “Neural machine translation with byte-level subwords”, Proceedings of the AAAI conference on artificial intelligence, Vol. 34, pages 9154-9160

S. Jastrzebski, M. Szymczak, S. Fort, D. Arpit, J. Tabor, K. Cho, and K. Geras. (2020), “The break-even point on optimization trajectories of deep neural networks”, ICLR

W. Falcon, and K. Cho. (2020), “A framework for contrastive self-supervised learning and designing a new approach”, arXiv preprint arXiv:2009.00104

R. Shu, J. Lee, H. Nakayama, and K. Cho. (2020), “Latent-variable non-autoregressive neural machine translation with deterministic inference using a delta posterior”, Proceedings of the aaai conference on artificial intelligence, Vol. 34, pages 8846-8853

K. Leung, B. Zhang, J. Tan, Y. Shen, K. J. Geras, J. S. Babb, K. Cho, G. Chang, and C. M. Deniz. (2020), “Prediction of total knee replacement and diagnosis of osteoarthritis by using deep learning on knee radiographs: data from the osteoarthritis initiative”, Radiology, Vol. 296, pages 584-593

N. Ng, K. Cho, and M. Ghassemi. (2020), “SSMBA: Self-supervised manifold based data augmentation for improving out-of-domain robustness”, EMNLP, pages 1268-1283

M. Li, Q. Zeng, Y. Lin, K. Cho, H. Ji, J. May, N. Chambers, and C. Voss. (2020), “Connecting the dots: Event graph schema induction with path language modeling”, Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 684-695

E. Zhang, N. Gupta, R. Nogueira, K. Cho, and J. Lin. (2020), “Rapidly deploying a neural search engine for the covid-19 open research dataset: Preliminary thoughts and lessons learned”, arXiv preprint arXiv:2004.05125

R. Tang, R. Nogueira, E. Zhang, N. Gupta, P. Cam, K. Cho, and J. Lin. (2020), “Rapidly bootstrapping a question answering dataset for COVID-19”, arXiv preprint arXiv:2004.11339

E. Zhang, N. Gupta, R. Tang, X. Han, R. Pradeep, K. Lu, Y. Zhang, R. Nogueira, K. Cho, H. Fang, and J. Lin. (2020), “Covidex: Neural ranking models and keyword search infrastructure for the covid-19 open research dataset”, SDP@EMNLP, pages 31-41

O. Marschall, K. Cho, and C. Savin. (2020), “A unified framework of online learning algorithms for training recurrent neural networks”, The Journal of Machine Learning Research, Vol. 21, pages 5320-5353

G. Kim, and K. Cho. (2020), “Length-adaptive transformer: Train once with length drop, use anytime with search”, ACL/IJCNLP, pages 6501-6511

S. Welleck, I. Kulikov, J. Kim, R. Y. Pang, and K. Cho. (2020), “Consistency of a recurrent language model with respect to incomplete decoding”, EMNLP, pages 5553-5568

J. E. Ortega, R. C. Mamani, and K. Cho. (2020), “Neural machine translation with a polysynthetic low resource language”, Machine Translation, Vol. 34, pages 325-346

C. Resnick, A. Gupta, J. Foerster, A. M. Dai, and K. Cho. (2020), “Capacity, Bandwidth, and Compositionality in Emergent Language Learning”, International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS)

H. Alberts, T. Huang, Y. Deshpande, Y. Liu, K. Cho, C. Vania, and I. Calixto. (2020), “Visualsem: a high-quality knowledge graph for vision and language”, arXiv preprint arXiv:2008.09150

K. Zołna, K. J. Geras, and K. Cho. (2020), “Classifier-agnostic saliency map extraction”, Computer Vision and Image Understanding

M. Nadeem, T. He, K. Cho, and J. Glass. (2020), “A systematic characterization of sampling algorithms for open-ended language generation”, AACL/IJCNLP, pages 334-346

B. Zhang, J. Tan, K. Cho, G. Chang, and C. M. Deniz. (2020), “Attention-based cnn for kl grade classification: Data from the osteoarthritis initiative”, 2020 IEEE 17th international symposium on biomedical imaging (ISBI), pages 731-735

J. Lee, R. Shu, and K. Cho. (2020), “Iterative refinement in the continuous space for non-autoregressive neural machine translation”, EMNLP, pages 1006-1015

S. Kurita, and K. Cho. (2020), “Generative language-grounded policy in vision-and-language navigation with bayes’ rule”, ICLR

W. F. Whitney, M. J. Song, D. Brandfonbrener, J. Altosaar, and K. Cho. (2020), “Evaluating representations by the complexity of learning low-loss predictors”, arXiv preprint arXiv:2009.07368

R. Nogueira, Z. Jiang, K. Cho, and J. Lin. (2020), “Navigation-based candidate expansion and pretrained language models for citation recommendation”, Scientometrics, Vol. 125, pages 3001-3016

N. Wu, S. Jastrzębski, J. Park, L. Moy, K. Cho, and K. J. Geras. (2020), “Improving the ability of deep neural networks to use information from multiple views in breast cancer screening”, Medical Imaging with Deep Learning, pages 827-842

J. Lee, D. Tran, O. Firat, and K. Cho. (2020), “On the discrepancy between density estimation and sequence generation”, SPNLP@EMNLP, pages 84-94

K. Kann, S. R. Bowman, and K. Cho. (2020), “Learning to Learn Morphological Inflection for Resource-Poor Languages”, AAAI

A. Gupta, C. Resnick, J. Foerster, A. Dai, and K. Cho. (2020), “Compositionality and capacity in emergent languages”, Proceedings of the 5th Workshop on Representation Learning for NLP, pages 34-38

J. A. Campos, K. Cho, A. Otegi, A. Soroa, G. Azkune, and E. Agirre. (2020), “Improving conversational question answering systems after deployment using feedback-weighted learning”, COLING, pages 2561-2571

R. Nogueira, Z. Jiang, K. Cho, and J. Lin. (2020), “Evaluating pretrained transformer models for citation recommendation”, CEUR Workshop Proceedings, Vol. 2591, pages 89-100

S. Jean, and K. Cho. (2020), “Log-Linear Reformulation of the Noisy Channel Model for Document-Level Neural Machine Translation”, Proceedings of the Fourth Workshop on Structured Prediction for NLP, pages 95-101

N. Wu, Z. Huang, Y. Shen, J. Park, J. Phang, T. Makino, S. Kim, K. Cho, L. Heacock, L. Moy, and K. J. Geras. (2020), “Reducing false-positive biopsies with deep neural networks that utilize local and global information in screening mammograms”, arXiv preprint arXiv:2009.09282

A. Góis, K. Cho, and A. Martins. (2020), “Learning Non-Monotonic Automatic Post-Editing of Translations from Human Orderings”, EAMT

E. J. Barezi, I. Calixto, K. Cho, and P. Fung. (2020), “A Study on the Autoregressive and non-Autoregressive Multi-label Learning”, arXiv preprint arXiv:2012.01711

C. Resnick, O. Litany, H. Larochelle, J. Bruna, and K. Cho. (2020), “Learned Equivariant Rendering without Transformation Supervision”, arXiv preprint arXiv:2011.05787

W. Oleszkiewicz, T. Makino, S. Jastrzębski, T. Trzciński, L. Moy, K. Cho, L. Heacock, and K. J. Geras. (2020), “Understanding the robustness of deep neural network classifiers for breast cancer screening”, arXiv preprint arXiv:2003.10041

J. Tan, B. Zhang, K. Cho, G. Chang, and C. M. Deniz. (2020), “Semi-supervised learning for predicting total knee replacement with unsupervised data augmentation”, Medical Imaging 2020: Computer-Aided Diagnosis, Vol. 11314, pages 153-161

I. Kostrikov, D. Yarats, and R. Fergus. (2020), “Image augmentation is all you need: Regularizing deep reinforcement learning from pixels”, ICLR

D. Yarats, I. Kostrikov, and R. Fergus. (2020), “Image augmentation is all you need: Regularizing deep reinforcement learning from pixels”, International conference on learning representations

R. Raileanu, M. Goldstein, D. Yarats, I. Kostrikov, and R. Fergus. (2020), “Automatic data augmentation for generalization in deep reinforcement learning”, arXiv preprint arXiv:2006.12862

Y. Du, J. Meier, J. Ma, R. Fergus, and A. Rives. (2020), “Energy-based models for atomic-resolution protein conformations”, ICLR

R. Raileanu, M. Goldstein, A. Szlam, and R. Fergus. (2020), “Fast adaptation via policy-dynamics value functions”, ICML, pages 7920-7931

K. Marino, R. Fergus, A. Szlam, and A. Gupta. (2020), “Empirically verifying hypotheses using reinforcement learning”, arXiv preprint arXiv:2006.15762

Z. Chen, L. Chen, S. Villar, and J. Bruna. (2020), “Can graph neural networks count substructures?”, Advances in neural information processing systems, Vol. 33, pages 10383-10395

F. Gama, J. Bruna, and A. Ribeiro. (2020), “Stability properties of graph neural networks”, IEEE Transactions on Signal Processing, Vol. 68, pages 5680-5695

M. Andreux, T. Angles, G. Exarchakisgeo, R. Leonardu, G. Rochette, L. Thiry, J. Zarka, S. Mallat, J. Andén, E. Belilovsky, J. Bruna, V. Lostanlen, M. Chaudhary, M. J. Hirn, E. Oyallon, S. Zhang, C. Cella, and M. Eickenberg. (2020), “Kymatio: Scattering transforms in python”, The Journal of Machine Learning Research, Vol. 21, pages 2256-2261

S. Cheng, Y. Ting, B. Ménard, and J. Bruna. (2020), “A new approach to observational cosmology using the scattering transform”, Monthly Notices of the Royal Astronomical Society, Vol. 499, pages 5902-5914

L. Chen, Z. Chen, and J. Bruna. (2020), “On graph neural networks versus graph-augmented mlps”, ICLR

C. Domingo-Enrich, S. Jelassi, A. Mensch, G. Rotskoff, and J. Bruna. (2020), “A mean-field analysis of two-player zero-sum games”, Advances in neural information processing systems, Vol. 33, pages 20215-20226

Z. Chen, G. Rotskoff, J. Bruna, and E. Vanden-Eijnden. (2020), “A dynamical central limit theorem for shallow neural networks”, Advances in Neural Information Processing Systems, Vol. 33, pages 22217-22230

Y. Arjevani, J. Bruna, B. Can, M. Gurbuzbalaban, S. Jegelka, and H. Lin. (2020), “Ideal: Inexact decentralized accelerated augmented lagrangian method”, Advances in Neural Information Processing Systems, Vol. 33, pages 20648-20659

J. d. Dios, and J. Bruna. (2020), “On sparsity in overparametrised shallow relu networks”, arXiv preprint arXiv:2006.10225

D. Rim, L. Venturi, J. Bruna, and B. Peherstorfer. (2020), “Depth separation for reduced deep networks in nonlinear model reduction: Distilling shock waves in nonlinear hyperbolic problems”, arXiv preprint arXiv:2007.13977

F. Gama, A. Ribeiro, and J. Bruna. (2020), “Stability of graph neural networks to relative perturbations”, ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 9070-9074

C. Heiß, R. Levie, C. Resnick, G. Kutyniok, and J. Bruna. (2020), “In-distribution interpretability for challenging modalities”, arXiv preprint arXiv:2007.00758

S. Jelassi, C. Domingo-Enrich, D. Scieur, A. Mensch, and J. Bruna. (2020), “Extra-gradient with player sampling for faster convergence in n-player games”, International Conference on Machine Learning, pages 4736-4745

A. Zweig, and J. Bruna. (2020), “Provably efficient third-person imitation from offline observation”, Conference on Uncertainty in Artificial Intelligence, pages 1228-1237

S. Jelassi, C. Domingo-Enrich, D. Scieur, A. Mensch, and J. Bruna. (2020), “Extra-gradient with player sampling for provable fast convergence in n-player games”, international conference on machine learning

C. Resnick, O. Litany, H. Larochelle, J. Bruna, and K. Cho. (2020), “Learned Equivariant Rendering without Transformation Supervision”, arXiv preprint arXiv:2011.05787

J. Bruna, E. Haber, G. Kutyniok, T. Pock, and R. Vidal. (2020), “Special Issue on the Mathematical Foundations of Deep Learning in Imaging Science”, Journal of Mathematical Imaging and Vision, Vol. 62, pages 277-278

L. F. Abbott, D. D. Bock, E. M. Callaway, W. Denk, C. Dulac, A. L. Fairhall, I. Fiete, K. M. Harris, M. Helmstaedter, V. Jain, N. Kasthuri, Y. LeCun, J. W. Lichtman, P. B. Littlewood, L. Luo, J. H. Maunsell, R. C. Reid, B. R. Rosen, G. M. Rubin, T. J. Sejnowski, H. S. Seung, K. Svoboda, D. W. Tank, D. Tsao, and D. Essen. (2020), “The mind of a mouse”, Cell, Vol. 182, pages 1372-1376

Y. LeCun, C. Cortes, and C. Burges. (2020), “MNIST handwritten digit database. AT&T Labs (2010)”, Volume 2Available online: http://yann. lecun. com/exdb/mnist (accessed on 4 December 2018)

L. Jing, J. Zbontar, and Y. LeCun. (2020), “Implicit Rank-Minimizing Autoencoder”, Neural Information Processing Systems (NeurIPS 2020)

Y. LeCun. (2020), “Self-supervised learning”, Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI, Invited Talk

2019

H. He, S. Zha, and H. Wang. (2019), “Unlearn dataset bias in natural language inference by fitting the residual”, DeepLo@EMNLP-IJCNLP, pages 132-142

H. He, N. Peng, and P. Liang. (2019), “Pun generation with surprise”, NAACL-HLT, pages 1734-1744

P. Rodriguez, S. Feng, M. Iyyer, H. He, and J. Boyd-Graber. (2019), “Quizbowl: The case for incremental question answering”, arXiv preprint arXiv:1904.04792

Y. Zhou, H. He, A. W. Black, and Y. Tsvetkov. (2019), “A dynamic strategy coach for effective negotiation”, SIGdial, pages 367-378

W. Zhang, X. Cui, U. Finkler, B. Kingsbury, G. Saon, D. Kung, and M. Picheny. (2019), “Distributed deep learning strategies for automatic speech recognition”, ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 5706-5710

S. Settle, K. Audhkhasi, K. Livescu, and M. Picheny. (2019), “Acoustically grounded word embeddings for improved acoustics-to-word speech recognition”, ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 5641-5645

K. Audhkhasi, G. Saon, Z. Tüske, B. Kingsbury, and M. Picheny. (2019), “Forget a Bit to Learn Better: Soft Forgetting for CTC-Based Automatic Speech Recognition.”, Interspeech, pages 2618-2622

A. W. Boggust, K. Audhkhasi, D. Joshi, D. Harwath, S. Thomas, R. S. Feris, D. Gutfreund, Y. Zhang, A. Torralba, M. Picheny, and J. R. Glass. (2019), “Grounding spoken words in unlabeled video.”, CVPR Workshops, Vol. 2

W. Zhang, X. Cui, U. Finkler, G. Saon, A. Kayi, A. Buyuktosunoglu, B. Kingsbury, D. Kung, and M. Picheny. (2019), “A highly efficient distributed deep learning system for automatic speech recognition”, INTERSPEECH, pages 2628-2632

S. Thomas, M. Suzuki, Y. Huang, G. Kurata, Z. Tuske, G. Saon, B. Kingsbury, M. Picheny, T. Dibert, A. Kaiser-Schatzlein, and B. Samko. (2019), “English broadcast news speech recognition by humans and machines”, ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 6455-6459

L. Sarı, S. Thomas, M. Hasegawa-Johnson, and M. Picheny. (2019), “Pre-training of speaker embeddings for low-latency speaker change detection in broadcast news”, ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 6286-6290

M. Picheny, Z. Tüske, B. Kingsbury, K. Audhkhasi, X. Cui, and G. Saon. (2019), “Challenging the boundaries of speech recognition: The malach corpus”, INTERSPEECH, pages 326-330

Z. Aldeneh, M. Jaiswal, M. Picheny, M. McInnis, and E. M. Provost. (2019), “Identifying mood episodes using dialogue features from clinical interviews”, INTERSPEECH, pages 1926-1930

S. Thomas, K. Audhkhasi, Z. Tüske, Y. Huang, and M. Picheny. (2019), “Detection and Recovery of OOVs for Improved English Broadcast News Captioning.”, Interspeech, pages 2973-2977

A. May, A. B. Garakani, Z. Lu, D. Guo, K. Liu, A. Bellet, L. Fan, M. Collins, D. Hsu, B. Kingsbury, M. Picheny, and F. Sha. (2019), “Kernel approximation methods for speech recognition”, The Journal of Machine Learning Research, Vol. 20, pages 2121-2156

K. C. Mac, X. Cui, W. Zhang, and M. Picheny. (2019), “Large-scale mixed-bandwidth deep neural network acoustic modeling for automatic speech recognition”, INTERSPEECH, pages 251-255

Y. Huang, S. Thomas, M. Suzuki, Z. Tüske, L. Sansone, and M. Picheny. (2019), “Semi-supervised training and data augmentation for adaptation of automatic broadcast news captioning systems”, 2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), pages 867-874

X. Cui, and M. Picheny. (2019), “Acoustic model optimization based on evolutionary stochastic gradient descent with anchors for automatic speech recognition”, INTERSPEECH, pages 1581-1585

G. Saon, Z. Tüske, K. Audhkhasi, B. Kingsbury, M. Picheny, and S. Thomas. (2019), “Simplified LSTMS for Speech Recognition”, 2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), pages 547-553

W. Maddox, T. Garipov, P. Izmailov, D. Vetrov, and A. G. Wilson. (2019), “A simple baseline for Bayesian uncertainty in deep learning”, Advances in Neural Information Processing Systems (NeurIPS)

C. Guo, J. R. Gardner, Y. You, A. G. Wilson, and K. Q. Weinberger. (2019), “Simple black-box adversarial attacks”, International Conference on Machine Learning (ICML)

B. Athiwaratkun, M. Finzi, P. Izmailov, and A. G. Wilson. (2019), “There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average”, International Conference on Learning Representations (ICLR)

R. Zhang, C. Li, J. Zhang, C. Chen, and A. G. Wilson. (2019), “Cyclical stochastic gradient MCMC for Bayesian deep learning”, International Conference on Learning Representations (ICLR)

K. A. Wang, G. Pleiss, J. R. Gardner, S. Tyree, K. Q. Weinberger, and A. G. Wilson. (2019), “Exact Gaussian processes on a million data points”, Advances in Neural Information Processing Systems (NeurIPS)

J. Wu, S. Toscano-Palmerin, P. I. Frazier, and A. G. Wilson. (2019), “Practical multi-fidelity bayesian optimization for hyperparameter tuning”, Uncertainty in Artificial Intelligence (UAI)

P. Izmailov, P. Kirichenko, M. Finzi, and A. G. Wilson. (2019), “Semi-supervised learning with normalizing flows”, International Conference on Machine Learning (ICML)

G. Yang, T. Zhang, P. Kirichenko, J. Bai, A. G. Wilson, and C. D. Sa. (2019), “SWALP: Stochastic Weight Averaging in Low-Precision Training”, International Conference on Machine Learning (ICML)

G. W. Benton, W. J. Maddox, J. P. Salkey, J. Albinati, and A. G. Wilson. (2019), “Function-space distributions over kernels”, Advances in Neural Information Processing Systems (NeurIPS)

I. A. Delbridge, D. S. Bindel, and A. G. Wilson. (2019), “Randomly Projected Additive Gaussian Processes for Regression”, International Conference on Machine Learning (ICML)

A. Ratner, D. Alistarh, G. Alonso, P. Bailis, S. Bird, N. Carlini, B. Catanzaro, E. Chung, B. Dally, and J. Dean. (2019), “Sysml: The new frontier of machine learning systems”, arXiv preprint arXiv:1904.03257

M. Finzi, P. Izmailov, W. Maddox, P. Kirichenko, and A. G. Wilson. (2019), “Invertible convolutional networks”, Workshop on Invertible Neural Nets and Normalizing Flows, International Conference on Machine Learning, Vol. 2

A. Ratner, D. Alistarh, G. Alonso, D. G. Andersen, P. Bailis, S. Bird, N. Carlini, B. Catanzaro, J. Chayes, E. Chung, B. Dally, J. Dean, I. S. Dhillon, A. Dimakis, P. Dubey, C. Elkan, G. Fursin, G. R. Ganger, L. Getoor, P. B. Gibbons, G. A. Gibson, J. E. Gonzalez, J. Gottschlich, S. Han, K. Hazelwood, F. Huang, M. Jaggi, K. Jamieson, M. I. Jordan, G. Joshi, R. Khalaf, J. Knight, J. Konečný, T. Kraska, A. Kumar, A. Kyrillidis, A. Lakshmiratan, J. Li, S. Madden, H. B. McMahan, E. Meijer, I. Mitliagkas, R. Monga, D. Murray, K. Olukotun, D. Papailiopoulos, G. Pekhimenko, T. Rekatsinas, A. Rostamizadeh, C. Ré, C. D. Sa, H. Sedghi, S. Sen, V. Smith, A. Smola, D. Song, E. Sparks, I. Stoica, V. Sze, M. Udell, J. Vanschoren, S. Venkataraman, R. Vinayak, M. Weimer, A. G. Wilson, E. Xing, M. Zaharia, C. Zhang, and A. Talwalkar. (2019), “Mlsys: The new frontier of machine learning systems”, arXiv preprint arXiv:1904.03257

W. Herlands, D. B. Neill, H. Nickisch, and A. G. Wilson. (2019), “Change Surfaces for Expressive Multidimensional Changepoints and Counterfactual Prediction.”, J. Mach. Learn. Res., Vol. 20, pages 99:1-99:51

W. J. Maddox, S. Tang, P. G. Moreno, A. G. Wilson, and A. Damianou. (2019), “On transfer learning via linearized neural networks”, NeurIPS Workshop on Meta-Learning (MetaLearn 2019)

D. Granziol, T. Garipov, S. Zohren, D. Vetrov, S. Roberts, and A. G. Wilson. (2019), “The Deep Learning Limit: are negative neural network eigenvalues just noise?”, ICML 2019 workshop on theoretical physics for deep learning

K. Huang, J. Altosaar, and R. Ranganath. (2019), “Clinicalbert: Modeling clinical notes and predicting hospital readmission”, arXiv preprint arXiv:1904.05342

F. D. Johansson, D. Sontag, and R. Ranganath. (2019), “Support and invertibility in domain-invariant representations”, AISTATS, pages 527-536

M. Ghassemi, T. Naumann, P. Schulam, A. L. Beam, I. Y. Chen, and R. Ranganath. (2019), “Practical guidance on artificial intelligence for health-care data”, The Lancet Digital Health, Vol. 1, pages e157-e159

M. McDermott, S. Wang, N. Marinsek, R. Ranganath, M. Ghassemi, and L. Foschini. (2019), “Reproducibility in machine learning for health”, RML@ICLR

J. Lawson, G. Tucker, B. Dai, and R. Ranganath. (2019), “Energy-inspired models: Learning with sampler-induced distributions”, Advances in Neural Information Processing Systems, Vol. 32

R. Singhal, X. Han, S. Lahlou, and R. Ranganath. (2019), “Kernelized complete conditional Stein discrepancy”, arXiv preprint arXiv:1904.04478

Z. Tavares, X. Zhang, E. Minaysan, J. Burroni, R. Ranganath, and A. S. Lezama. (2019), “The random conditional distribution for higher-order probabilistic inference”, arXiv preprint arXiv:1903.10556

G. E. Moran, D. M. Blei, and R. Ranganath. (2019), “Population predictive checks”, arXiv preprint arXiv:1908.00882

Z. Tavares, J. Burroni, E. Minasyan, A. Solar-Lezama, and R. Ranganath. (2019), “Predicate exchange: Inference with declarative knowledge”, International Conference on Machine Learning, pages 6186-6195

D. Tang, and R. Ranganath. (2019), “The variational predictive natural gradient”, International Conference on Machine Learning, pages 6145-6154

X. Han, Y. Hu, L. Foschini, L. Chinitz, L. Jankelson, and R. Ranganath. (2019), “Adversarial examples for electrocardiograms”, arXiv preprint arXiv:1905.05163

Z. Tavares, J. Burroni, E. Minaysan, A. S. Lezama, and R. Ranganath. (2019), “Soft constraints for inference with declarative knowledge”, arXiv preprint arXiv:1901.05437

M. B. McDermott, S. Wang, N. Marinsek, R. Ranganath, M. Ghassemi, and L. Foschini. (2019), “Conceptual Replicability”, arXiv preprint arXiv:1907.01463

M. B. McDermott, S. Wang, N. Marinsek, R. Ranganath, M. Ghassemi, and L. Foschini. (2019), “Statistical Replicability”, CogSci

B. M. Lake. (2019), “Compositional generalization through meta sequence-to-sequence learning”, Advances in Neural Information Processing Systems 32

B. M. Lake, T. Linzen, and M. Baroni. (2019), “Human few-shot learning of compositional instructions”, Proceedings of the 41st Annual Conference of the Cognitive Science Society

Z. Wang, and B. M. Lake. (2019), “Modeling question asking using neural program generation”, arXiv preprint arXiv:1907.09899

R. Feinman, and B. M. Lake. (2019), “Learning a smooth kernel regularizer for convolutional neural networks”, CogSci, pages 1710-1716

A. Rothe, B. M. Lake, and T. M. Gureckis. (2019), “Asking goal-oriented questions and learning from answers”, Proceedings of the 41st Annual Conference of the Cognitive Science Society.

A. E. Orhan, and B. M. Lake. (2019), “Improving the robustness of ImageNet classifiers using elements of human visual cognition”, arXiv preprint arXiv:1906.08416

A. E. Orhan, and B. M. Lake. (2019), “Improving the robustness of ImageNet classifiers using elements of human visual cognition”, arXiv e-prints, pages arXiv: 1906.08416

A. Wang, A. Singh, J. Michael, F. Hill, O. Levy, and S. R. Bowman. (2019), “GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding”, Proceedings of ICLR

A. Wang, Y. Pruksachatkun, N. Nangia, A. Singh, J. Michael, F. Hill, O. Levy, and S. R. Bowman. (2019), “SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems”, Proceedings of NeurIPS

A. Warstadt, A. Singh, and S. R. Bowman. (2019), “Neural network acceptability judgments”, TACL, Vol. 7, pages 625-641

I. Tenney, P. Xia, B. Chen, A. Wang, A. Poliak, R. T. McCoy, N. Kim, B. V. Durme, S. Bowman, D. Das, and E. Pavlick. (2019), “What do you learn from context? Probing for sentence structure in contextualized word representations”, Proceedings of ICLR

C. May, A. Wang, S. Bordia, S. R. Bowman, and R. Rudinger. (2019), “On Measuring Social Biases in Sentence Encoders”, Proceedings of NAACL-HLT

S. Bordia, and S. R. Bowman. (2019), “Identifying and Reducing Gender Bias in Word-Level Language Models”, Proceedings of the NAACL-HLT Student Research Workshop

A. Wang, J. Hula, P. Xia, R. Pappagari, R. T. McCoy, R. Patel, N. Kim, I. Tenney, Y. Huang, K. Yu, S. Jin, B. Chen, B. V. Durme, É. Grave, E. Pavlick, and S. Bowman. (2019), “Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling”, Proceedings of ACL, pages 4465-4476

P. M. Htut, J. Phang, S. Bordia, and S. R. Bowman. (2019), “Do Attention Heads in BERT Track Syntactic Dependencies?”, arXiv preprint 1911.12246

A. Warstadt, Y. Cao, I. Grosu, W. Peng, H. Blix, Y. Nie, A. Alsop, S. Bordia, H. Liu, A. Parrish, S. Wang, J. Phang, A. Mohananey, P. M. Htut, P. Jeretič, and S. R. Bowman. (2019), “Investigating BERT’s Knowledge of Language: Five Analysis Methods with NPIs”, Proceedings of EMNLP

N. Kim, R. Patel, A. Poliak, A. Wang, P. Xia, R. T. McCoy, I. Tenney, A. Ross, T. Linzen, B. V. Durme, S. R. Bowman, and E. Pavlick. (2019), “Probing What Different NLP Tasks Teach Machines about Function Word Comprehension”, Proceedings of *SEM

N. Nangia, and S. R. Bowman. (2019), “Human vs. Muppet: A Conservative Estimate of Human Performance on the GLUE Benchmark”, Proceedings of ACL

K. Kann, K. Cho, and S. R. Bowman. (2019), “Towards Realistic Practices In Low-Resource Natural Language Processing: The Development Set”, Proceedings of EMNLP

A. Warstadt, and S. R. Bowman. (2019), “Grammatical Analysis of Pretrained Sentence Encoders with Acceptability Judgments”, arXiv preprint 1901.03438

N. Subramani, S. Bowman, and K. Cho. (2019), “Can Unconditional Language Models Recover Arbitrary Sentences?”, Proceedings of NeurIPS

S. Bowman, and X. Zhu. (2019), “Deep Learning for Natural Language Inference”, NAACL-HLT Tutorial, pages 6-8

K. Kann, A. Mohananey, S. Bowman, and K. Cho. (2019), “Neural unsupervised parsing beyond english”, Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019), pages 209-218

P. M. Htut, K. Cho, and S. R. Bowman. (2019), “Inducing constituency trees through neural machine translation”, arXiv preprint arXiv:1909.10056

R. Nogueira, and K. Cho. (2019), “Passage Re-ranking with BERT”, IEEE Access, pages 213232-213243

M. Kisantal, Z. Wojna, J. Murawski, J. Naruniec, and K. Cho. (2019), “Augmentation for small object detection”, Sensors, pages 7663

N. Wu, J. Phang, J. Park, Y. Shen, Z. Huang, M. Zorin, S. Jastrzębski, T. Févry, J. Katsnelson, E. Kim, S. Wolfson, U. Parikh, S. Gaddam, L. Lin, K. Ho, J. D. Weinstein, B. Reig, Y. Gao, H. Toth, K. Pysarenko, A. Lewin, J. Lee, K. Airola, E. Mema, S. Chung, E. Hwang, N. Samreen, S. G. Kim, L. Heacock, L. Moy, K. Cho, and K. J. Geras. (2019), “Deep neural networks improve radiologists’ performance in breast cancer screening”, IEEE transactions on medical imaging, Vol. 39, pages 1184-1194

S. Welleck, I. Kulikov, S. Roller, E. Dinan, K. Cho, and J. Weston. (2019), “Neural text generation with unlikelihood training”, EACL, pages 1432-1441

A. Wang, and K. Cho. (2019), “BERT has a mouth, and it must speak: BERT as a Markov random field language model”, arXiv preprint arXiv:1902.04094

R. Nogueira, W. Yang, J. Lin, and K. Cho. (2019), “Document expansion by query prediction”, arXiv preprint arXiv:1904.08375

R. Nogueira, W. Yang, K. Cho, and J. Lin. (2019), “Multi-stage document ranking with BERT”, arXiv preprint arXiv:1910.14424

S. Welleck, J. Weston, A. Szlam, and K. Cho. (2019), “Dialogue natural language inference”, ACL

G. Louppe, K. Cho, C. Becot, and K. Cranmer. (2019), “QCD-aware recursive neural networks for jet physics”, Journal of High Energy Physics

C. Lee, K. Cho, and W. Kang. (2019), “Mixout: Effective regularization to finetune large-scale pretrained language models”, ICLR

E. Mansimov, O. Mahmood, S. Kang, and K. Cho. (2019), “Molecular geometry prediction using a deep generative graph neural network”, Scientific reports, Vol. 9, pages 20381

E. Grefenstette, B. Amos, D. Yarats, P. M. Htut, A. Molchanov, F. Meier, D. Kiela, K. Cho, and S. Chintala. (2019), “Generalized inner loop meta-learning”, arXiv preprint arXiv:1910.01727

S. Welleck, K. Brantley, H. Daumé III, and K. Cho. (2019), “Non-Monotonic Sequential Text Generation”, International Conference on Machine Learning

S. Golkar, M. Kagan, and K. Cho. (2019), “Continual learning via neural pruning”, arXiv preprint arXiv:1903.04476

J. Gu, Q. Liu, and K. Cho. (2019), “Insertion-based decoding with automatically inferred generation order”, TACL

J. Gu, Y. Wang, K. Cho, and V. O. Li. (2019), “Improved Zero-shot Neural Machine Translation via Ignoring Spurious Correlations”, ACL

L. Graesser, K. Cho, and D. Kiela. (2019), “Emergent linguistic phenomena in multi-agent communication games”, EMNLP

K. Kann, K. Cho, and S. R. Bowman. (2019), “Towards Realistic Practices In Low-Resource Natural Language Processing: The Development Set”, EMNLP

W. Whitney, R. Agarwal, K. Cho, and A. Gupta. (2019), “Dynamics-aware embeddings”, EPJ Data Sci., pages 22

J. Lee, K. Cho, and D. Kiela. (2019), “Countering Language Drift via Visual Grounding”, EMNLP

E. Mansimov, A. Wang, S. Welleck, and K. Cho. (2019), “A generalized framework of sequence generation with application to undirected sequence models”, arXiv preprint arXiv:1905.12790

I. Drori, Y. Krishnamurthy, R. Lourenco, R. Rampin, K. Cho, C. Silva, and J. Freire. (2019), “Automatic machine learning by pipeline synthesis using model-based reinforcement learning and a grammar”, arXiv preprint arXiv:1905.10345

R. Shu, H. Nakayama, and K. Cho. (2019), “Generating diverse translations with sentence codes”, Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1823-1827

E. Perez, S. Karamcheti, R. Fergus, J. Weston, D. Kiela, and K. Cho. (2019), “Finding Generalizable Evidence by Learning to Convince Q&A Models”, EMNLP

Y. Shen, N. Wu, J. Phang, J. Park, G. Kim, L. Moy, K. Cho, and K. J. Geras. (2019), “Globally-aware multiple instance classifier for breast cancer screening”, Machine Learning in Medical Imaging: 10th International Workshop, MLMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings 10, pages 18-26

K. Choi, and K. Cho. (2019), “Deep Unsupervised Drum Transcription”, ISMIR

S. Jean, and K. Cho. (2019), “Context-Aware Learning for Neural Machine Translation”, Workshop on Neural Generation and Translation

J. Oh, K. Cho, and J. Bruna. (2019), “Advancing graphsage with a data-driven node sampling”, arXiv preprint arXiv:1904.12935

N. Subramani, S. Bowman, and K. Cho. (2019), “Can Unconditional Language Models Recover Arbitrary Sentences?”, NeurIPS

J. Zhao, and K. Cho. (2019), “Retrieval-augmented convolutional neural networks for improved robustness against adversarial examples”, Conference on Computer Vision and Pattern Recognition (CVPR)

T. Wang, and K. Cho. (2019), “Attention-based Mixture Density Recurrent Networks for History-based Recommendation”, 1st International Workshop on Deep Learning Practice for High-Dimensional Sparse Data with KDD 2019

D. Bahdanau, K. Cho, and Y. Bengio. (2019), “Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv: 14090473. 2014”, Search in

T. Févry, J. Phang, N. Wu, S. Kim, L. Moy, K. Cho, and K. J. Geras. (2019), “Improving localization-based approaches for breast cancer screening exam classification”, arXiv preprint arXiv:1908.00615

P. M. Htut, K. Cho, and S. R. Bowman. (2019), “Inducing constituency trees through neural machine translation”, arXiv preprint arXiv:1909.10056

I. Kulikov, J. Lee, and K. Cho. (2019), “Multi-turn beam search for neural dialogue modeling”, arXiv preprint arXiv:1906.00141

T. He, J. Liu, K. Cho, M. Ott, B. Liu, J. Glass, and F. Peng. (2019), “Analyzing the forgetting problem in the pretrain-finetuning of dialogue response models”, EACL, pages 1121-1133

J. Park, J. Phang, Y. Shen, N. Wu, S. Kim, L. Moy, K. Cho, and K. J. Geras. (2019), “Screening Mammogram Classification with Prior Exams”, arXiv preprint arXiv:1907.13057

O. Marschall, K. Cho, and C. Savin. (2019), “Using local plasticity rules to train recurrent neural networks”, arXiv preprint arXiv:1905.12100

O. Marschall, K. Cho, and C. Savin. (2019), “Evaluating biological plausibility of learning algorithms the lazy way”, Real Neurons {\&} Hidden Units: Future directions at the intersection of neuroscience and artificial intelligence@ NeurIPS 2019

S. Welleck, and K. Cho. (2019), “Sequential graph dependency parser”, RANLP, pages 1338-1345

P. M. Htut, K. Cho, and S. R. Bowman. (2019), “Inducing Constituency Trees through Neural Machine Translation”, arXiv e-prints, pages arXiv: 1909.10056

S. Golkar, and K. Cho. (2019), “Task-Driven Data Verification via Gradient Descent”, arXiv preprint arXiv:1905.05843

E. Perez, S. Karamcheti, R. Fergus, J. Weston, D. Kiela, and K. Cho. (2019), “Finding generalizable evidence by learning to convince q&a models”, EMNLP/IJCNLP, pages 2402-2411

Z. Chen, S. Villar, L. Chen, and J. Bruna. (2019), “On the equivalence between graph isomorphism testing and function approximation with gnns”, Advances in neural information processing systems, Vol. 32

L. Venturi, A. S. Bandeira, and J. Bruna. (2019), “Spurious valleys in one-hidden-layer neural network optimization landscapes”, Journal of Machine Learning Research, Vol. 20, pages 133

F. Williams, T. Schneider, C. Silva, D. Zorin, J. Bruna, and D. Panozzo. (2019), “Deep geometric prior for surface reconstruction”, Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10130-10139

F. Williams, M. Trager, D. Panozzo, C. Silva, D. Zorin, and J. Bruna. (2019), “Gradient dynamics of shallow univariate relu networks”, Advances in Neural Information Processing Systems, Vol. 32

F. Gama, A. Ribeiro, and J. Bruna. (2019), “Stability of graph scattering transforms”, Advances in Neural Information Processing Systems, Vol. 32

J. Kileel, M. Trager, and J. Bruna. (2019), “On the expressive power of deep polynomial neural networks”, Advances in neural information processing systems, Vol. 32

G. Rotskoff, S. Jelassi, J. Bruna, and E. Vanden-Eijnden. (2019), “Global convergence of neuron birth-death dynamics”, arXiv preprint arXiv:1902.01843

J. Bruna, and S. Mallat. (2019), “Multiscale sparse microcanonical models”, Mathematical Statistics and Learning, Vol. 1, pages 257-315

S. d’Ascoli, L. Sagun, G. Biroli, and J. Bruna. (2019), “Finding the needle in the haystack with convolutions: on the benefits of architectural bias”, Advances in Neural Information Processing Systems, Vol. 32

M. Trager, K. Kohn, and J. Bruna. (2019), “Pure and spurious critical points: a geometric study of linear networks”, ICLR

J. Oh, K. Cho, and J. Bruna. (2019), “Advancing graphsage with a data-driven node sampling”, arXiv preprint arXiv:1904.12935

D. Brandfonbrener, and J. Bruna. (2019), “Geometric insights into the convergence of nonlinear TD learning”, ICLR

T. Frerix, and J. Bruna. (2019), “Approximating orthogonal matrices with effective Givens factorization”, International Conference on Machine Learning, pages 1993-2001

L. Chen, S. Gong, J. Bruna, and M. Bronstein. (2019), “Attributed random walk as matrix factorization”, neural information processing systems, graph representation learning workshop

M. d. Hoop, R. Baraniuk, J. Bruna, M. Campillo, H. Jasperson, S. Mallat, T. Nguyen, and L. Seydoux. (2019), “Unsupervised learning for identification of seismic signals.”, Geophysical Research Abstracts, Vol. 21

C. D. Enrich, S. Jelassi, C. Domingo-Enrich, D. Scieur, A. Mensch, and J. Bruna. (2019), “Extragradient with player sampling for faster Nash equilibrium finding”, arXiv e-prints, pages arXiv: 1905.12363

R. Groscot, J. Bruna, and L. D. Cohen. (2019), “Volumetric Meshes: a Neural Network-friendly representation for 3D shapes generative models”, In Workshop I: Geometric Processing, Part of the IPAM Long Program Geometry and Learning from Data in 3D and Beyond

B. Neyshabur, Z. Li, S. Bhojanapalli, Y. LeCun, and N. Srebro. (2019), “The role of over-parametrization in generalization of neural networks”, international conference on learning representations (ICLR 2019)

Y. LeCun. (2019), “Deep Learning Hardware: Past, Present, and Future”, 2019 IEEE International Solid-State Circuits Conference-(ISSCC), pages 12-19

M. Henaff, A. Canziani, and Y. LeCun. (2019), “Model-Predictive Policy Learning with Uncertainty Regularization for Driving in Dense Traffic”, International Conference on Learning Representations (ICLR 2019)

H. V. Vo, F. Bach, M. Cho, K. Han, Y. LeCun, P. Pérez, and J. Ponce. (2019), “Unsupervised image matching and object discovery as optimization”, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8287-8296

C. Wu, M. Tygert, and Y. LeCun. (2019), “A hierarchical loss and its problems when classifying non-hierarchically”, Plos one, Vol. 14, pages e0226222

M. Belghazi, M. Oquab, Y. LeCun, and D. Lopez-Paz. (2019), “Learning about an exponential amount of conditional distributions”, Advances in Neural Information Processing Systems, pages 13703-13714

G. Hinton, and Y. LeCun. (2019), “The deep learning revolution”, Turing Lecture at FCRC

2018

C. Naesseth, S. Linderman, R. Ranganath, and D. Blei. (2018), “Variational sequential monte carlo”, International conference on artificial intelligence and statistics, pages 968-977

M. Ghassemi, T. Naumann, P. Schulam, A. L. Beam, and R. Ranganath. (2018), “Opportunities in machine learning for healthcare”, arXiv preprint arXiv:1806.00388

X. Miscouridou, A. Perotte, N. Elhadad, and R. Ranganath. (2018), “Deep survival analysis: Nonparametrics and missingness”, Machine Learning for Healthcare Conference, pages 244-256

R. Ranganath, and A. Perotte. (2018), “Multiple causal inference with latent confounding”, arXiv preprint arXiv:1805.08273

R. Ranganath, and D. M. Blei. (2018), “Correlated random measures”, Journal of the American statistical Association, Vol. 113, pages 417-430

A. B. Dieng, R. Ranganath, J. Altosaar, and D. Blei. (2018), “Noisin: Unbiased regularization for recurrent neural networks”, International Conference on Machine Learning, pages 1252-1261

J. Altosaar, R. Ranganath, and D. Blei. (2018), “Proximity variational inference”, International Conference on Artificial Intelligence and Statistics, pages 1961-1969

R. G. Krishnan, A. Khandelwal, R. Ranganath, and D. A. Sontag. (2018), “Max-margin learning with the Bayes factor.”, UAI, pages 896-905

B. M. Lake, and M. Baroni. (2018), “Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks”, International Conference on Machine Learning (ICML)

J. Loula, M. Baroni, and B. M. Lake. (2018), “Rearranging the familiar: Testing compositional generalization in recurrent networks”, BlackboxNLP@EMNLP, pages 108-114

A. Rothe, B. M. Lake, and T. M. Gureckis. (2018), “Do people ask good questions?”, Computational Brain & Behavior, Vol. 1, pages 69-89

R. Feinman, and B. M. Lake. (2018), “Learning Inductive Biases with Simple Neural Networks”, Proceedings of the 40th Annual Conference of the Cognitive Science Society

B. M. Lake, N. D. Lawrence, and J. B. Tenenbaum. (2018), “The emergence of organizing structure in conceptual representation”, Cognitive Science, Vol. 42, pages 809-832

A. Williams, N. Nangia, and S. R. Bowman. (2018), “A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference”, Proceedings of NAACL-HLT

S. Gururangan, S. Swayamdipta, O. Levy, R. Schwartz, S. R. Bowman, and N. A. Smith. (2018), “Annotation artifacts in natural language inference data”, Proceedings of NAACL

A. Conneau, G. Lample, R. Rinott, A. Williams, S. R. Bowman, H. Schwenk, and V. Stoyanov. (2018), “XNLI: Evaluating Cross-lingual Sentence Representations”, Proceedings of EMNLP

J. Phang, T. Févry, and S. R. Bowman. (2018), “Sentence encoders on STILTs: Supplementary training on intermediate labeled-data tasks”, arXiv preprint 1811.01088

A. Williams, A. Drozdov, and S. R. Bowman. (2018), “Do latent tree learning models identify meaningful structure in sentences?”, Transactions of the Association for Computational Linguistics, Vol. 6, pages 253-267

K. Zhang, and S. Bowman. (2018), “Language Modeling Teaches You More than Translation Does: Lessons Learned Through Auxiliary Task Analysis”, Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, pages 359-361

N. Nangia, and S. R. Bowman. (2018), “ListOps: A Diagnostic Dataset for Latent Tree Learning”, Proceedings of the NAACL-HLT Student Research Workshop

P. M. Htut, K. Cho, and S. R. Bowman. (2018), “Grammar Induction with Neural Language Models: An Unusual Replication”, Proceedings of EMNLP

P. M. Htut, S. R. Bowman, and K. Cho. (2018), “Training a ranking function for open-domain question answering”, Proceedings of the NAACL Student Research Workshop

Y. Gong, and S. R. Bowman. (2018), “Ruminating reader: Reasoning with gated multi-hop attention”, Proceedings of the Workshop on Machine Reading for Question Answering

K. Kann, A. Warstadt, A. Williams, and S. R. Bowman. (2018), “Verb argument structure alternations in word and sentence embeddings”, Proceedings of SCiL

Y. Chen, V. O. Li, K. Cho, and S. R. Bowman. (2018), “A Stable and Effective Learning Strategy for Trainable Greedy Decoding”, Proceedings of EMNLP

W. Chung, S. Wang, and S. R. Bowman. (2018), “The Lifted Matrix-Space Model for Semantic Composition”, Proceedings of CoNLL

G. Dinu, M. Ballesteros, A. Sil, S. Bowman, W. Hamza, A. Søgaard, T. Naseem, and Y. Goldberg. (2018), “Proceedings of the Workshop on the Relevance of Linguistic Structure in Neural Architectures for NLP”, Proceedings of the Workshop on the Relevance of Linguistic Structure in Neural Architectures for NLP

Z. Che, S. Purushotham, K. Cho, D. Sontag, and Y. Liu. (2018), “Recurrent neural networks for multivariate time series with missing values”, Scientific reports, Vol. 8, pages 6085

M. Artetxe, G. Labaka, E. Agirre, and K. Cho. (2018), “Unsupervised neural machine translation”, ICLR

J. Lee, E. Mansimov, and K. Cho. (2018), “Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative Refinement”, EMNLP

J. Gu, Y. Wang, Y. Chen, K. Cho, and V. O. Li. (2018), “Meta-Learning for Low-Resource Neural Machine Translation”, EMNLP

R. D. Hjelm, A. P. Jacob, A. Trischler, G. Che, K. Cho, and Y. Bengio. (2018), “Boundary Seeking GANs”, ICLR

J. Gu, Y. Wang, K. Cho, and V. O. Li. (2018), “Search engine guided neural machine translation”, Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 32

L. Huang, H. Ji, K. Cho, and C. R. Voss. (2018), “Zero-Shot Transfer Learning for Event Extraction”, ACL

H. Choi, K. Cho, and Y. Bengio. (2018), “Fine-grained attention mechanism for neural machine translation”, Neurocomputing, Vol. 284, pages 171-176

S. Kang, and K. Cho. (2018), “Conditional molecular design with deep generative models”, Journal of Chemical Information and Modeling

C. M. Deniz, S. Xiang, R. S. Hallyburton, A. Welbeck, J. S. Babb, S. Honig, K. Cho, and G. Chang. (2018), “Segmentation of the proximal femur from MR images using deep convolutional neural networks”, Scientific reports, Vol. 8, pages 16485

X. Gu, K. Cho, J. Ha, and S. Kim. (2018), “DialogWAE: Multimodal response generation with conditional Wasserstein auto-encoder”, ICLR

D. Kiela, C. Wang, and K. Cho. (2018), “Dynamic Meta-Embeddings for Improved Sentence Representations”, EMNLP

I. Kulikov, A. H. Miller, K. Cho, and J. Weston. (2018), “Importance of search and evaluation strategies in neural dialogue modeling”, INLG, pages 76-87

C. Resnick, W. Eldridge, D. Ha, D. Britz, J. Foerster, J. Togelius, K. Cho, and J. Bruna. (2018), “Pommerman: A multi-agent playground”, AIIDE Workshops

N. Wu, K. J. Geras, Y. Shen, J. Su, S. G. Kim, E. Kim, S. Wolfson, L. Moy, and K. Cho. (2018), “Breast density classification with deep convolutional neural networks”, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 6682-6686

K. Choi, G. Fazekas, M. Sandler, and K. Cho. (2018), “A comparison of audio signal preprocessing methods for deep neural networks on music tagging”, 2018 26th European Signal Processing Conference (EUSIPCO), pages 1870-1874

J. Bastings, M. Baroni, J. Weston, K. Cho, and D. Kiela. (2018), “Jump to better conclusions: SCAN both left and right”, BlackboxNLP@EMNLP, pages 47-55

C. Gulcehre, S. Chandar, K. Cho, and Y. Bengio. (2018), “Dynamic neural turing machine with continuous and discrete addressing schemes”, Neural computation, Vol. 30, pages 857-884

S. M. Plis, M. F. Amin, A. Chekroud, D. Hjelm, E. Damaraju, H. J. Lee, J. R. Bustillo, K. Cho, G. D. Pearlson, and V. D. Calhoun. (2018), “Reading the (functional) writing on the (structural) wall: Multimodal fusion of brain structure and function via a deep neural network based translation approach reveals novel impairments in schizophrenia”, NeuroImage, Vol. 181, pages 734-747

P. M. Htut, K. Cho, and S. R. Bowman. (2018), “Grammar Induction with Neural Language Models: An Unusual Replication”, EMNLP

P. M. Htut, S. R. Bowman, and K. Cho. (2018), “Training a ranking function for open-domain question answering”, NAACL-HLT, pages 120-127

K. Choi, G. Fazekas, K. Cho, and M. Sandler. (2018), “The effects of noisy labels on deep convolutional neural networks for music tagging”, IEEE Transactions on Emerging Topics in Computational Intelligence, Vol. 2, pages 139-149

C. Resnick, R. Raileanu, S. Kapoor, A. Peysakhovich, K. Cho, and J. Bruna. (2018), “Backplay: “Man muss immer umkehren””, arXiv preprint arXiv:1807.06919

Y. Chen, V. O. Li, K. Cho, and S. R. Bowman. (2018), “A Stable and Effective Learning Strategy for Trainable Greedy Decoding”, EMNLP

C. Wang, K. Cho, and D. Kiela. (2018), “Code-switched named entity recognition with embedding attention”, Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching, pages 154-158

C. Lee, K. Cho, and W. Kang. (2018), “Directional Analysis of Stochastic Gradient Descent via von Mises-Fisher Distributions in Deep learning”, arXiv

R. D. Hjelm, E. Damaraju, K. Cho, H. Laufs, S. M. Plis, and V. D. Calhoun. (2018), “Spatio-temporal dynamics of intrinsic networks in functional magnetic imaging data using recurrent neural networks”, Frontiers in neuroscience, Vol. 12, pages 600

R. Han, A. Spirling, M. Gill, and K. Cho. (2018), “Conditional Word Embedding and Hypothesis Testing via Bayes-by-Backprop”, EMNLP

S. Welleck, Z. Yao, Y. Gai, J. Mao, Z. Zhang, and K. Cho. (2018), “Loss Functions for Multiset Prediction”, NIPS

L. Huang, K. Cho, B. Zhang, H. Ji, and K. Knight. (2018), “Multi-lingual Common Semantic Space Construction via Cluster-consistent Word Embedding”, EMNLP

N. Weber, L. Shekhar, N. Balasubramanian, and K. Cho. (2018), “Controlling decoding for more abstractive summaries with copy-based networks”, arXiv preprint arXiv:1803.07038

C. Resnick, I. Kulikov, K. Cho, and J. Weston. (2018), “Vehicle communication strategies for simulated highway driving”, arXiv preprint arXiv:1804.07178

K. Kann, S. Lauly, and K. Cho. (2018), “The NYU system for the CoNLL–SIGMORPHON 2018 shared task on universal morphological reinflection”, Proceedings of the CoNLL–SIGMORPHON 2018 Shared Task: Universal Morphological Reinflection, pages 58-63

J. E. Ortega, W. Liu, A. Meyers, and K. Cho. (2018), “Letting a neural network decide which machine translation system to use for black-box fuzzy-match repair”, Proceedings of the 21st Annual Conference of the European Association for Machine Translation, pages 229-238

R. F. Nogueira, K. Cho, and C. G. Scholar. (2018), “New York University at TREC 2018 Complex Answer Retrieval Track.”, TREC

P. M. Htut, K. Cho, and S. R. Bowman. (2018), “Grammar Induction with Neural Language Models: An Unusual Replication”, EMNLP, pages 4998-5003

P. M. Htut, S. R. Bowman, and K. Cho. (2018), “Training a Ranking Function for Open-Domain Question Answering”, NAACL-HLT, pages 120-127

E. Denton, and R. Fergus. (2018), “Stochastic video generation with a learned prior”, International conference on machine learning, pages 1174-1183

R. Raileanu, E. Denton, A. Szlam, and R. Fergus. (2018), “Modeling others using oneself in multi-agent reinforcement learning”, International conference on machine learning, pages 4257-4266

I. Misra, R. Girshick, R. Fergus, M. Hebert, A. Gupta, and L. Maaten. (2018), “Learning by asking questions”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 11-20

R. Riochet, M. Y. Castro, M. Bernard, A. Lerer, R. Fergus, V. Izard, and E. Dupoux. (2018), “Intphys: A framework and benchmark for visual intuitive physics reasoning”, arXiv preprint arXiv:1803.07616

A. Zhang, S. Sukhbaatar, A. Lerer, A. Szlam, and R. Fergus. (2018), “Composable planning with attributes”, International Conference on Machine Learning, pages 5842-5851

S. Sukhbaatar, E. Denton, A. Szlam, and R. Fergus. (2018), “Learning goal embeddings via self-play for hierarchical reinforcement learning”, arXiv preprint arXiv:1811.09083

K. Marino, A. Gupta, R. Fergus, and A. Szlam. (2018), “Hierarchical RL using an ensemble of proprioceptive periodic policies”, International Conference on Learning Representations

A. Nowak, S. Villar, A. S. Bandeira, and J. Bruna. (2018), “Revised note on learning quadratic assignment with graph neural networks”, 2018 IEEE Data Science Workshop (DSW), pages 1-5

F. Gama, A. Ribeiro, and J. Bruna. (2018), “Diffusion scattering transforms on graphs”, ICLR

I. Kostrikov, Z. Jiang, D. Panozzo, D. Zorin, and J. Bruna. (2018), “Surface networks”, Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2540-2548

C. Resnick, W. Eldridge, D. Ha, D. Britz, J. Foerster, J. Togelius, K. Cho, and J. Bruna. (2018), “Pommerman: A multi-agent playground”, AIIDE Workshops

N. Choma, F. Monti, L. Gerhardt, T. Palczewski, Z. Ronaghi, P. Prabhat, W. Bhimji, M. M. Bronstein, S. R. Klein, and J. Bruna. (2018), “Graph neural networks for icecube signal classification”, 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), pages 386-391

C. Resnick, R. Raileanu, S. Kapoor, A. Peysakhovich, K. Cho, and J. Bruna. (2018), “Backplay:” man muss immer umkehren””, arXiv preprint arXiv:1807.06919

A. Nowak, D. Folqué, and J. Bruna. (2018), “Divide and conquer networks”, International Conference on Learning Representations

M. Bronstein, X. Bresson, A. Szlam, J. Bruna, and Y. LeCun. (2018), “Tutorial: Geometric deep learning on graphs and manifolds”, Society for Industrial and Applied Mathematics (SIAM)

D. Folqué, S. Sukhbaatar, A. Szlam, and J. Bruna. (2018), “Planning with Arithmetic and Geometric Attributes”, arXiv preprint arXiv:1809.02031

J. Zhao, Y. Kim, K. Zhang, A. M. Rush, and Y. LeCun. (2018), “Adversarially Regularized Autoencoders for Generating Discrete Structures”, International Conference on Machine Learning (ICML 2018)

M. Baity-Jesi, L. Sagun, M. Geiger, S. Spigler, G. B. Arous, C. Cammarota, Y. LeCun, M. Wyart, and G. Biroli. (2018), “Comparing Dynamics: Deep Neural Networks versus Glassy Systems”, International Conference on Machine Learning (ICML 2018)

O. Sbai, M. Elhoseiny, A. Bordes, Y. LeCun, and C. Couprie. (2018), “Design: Design inspiration from generative networks”, Proceedings of the European Conference on Computer Vision (ECCV) Workshops, pages 0-0

P. Luc, C. Couprie, Y. Lecun, and J. Verbeek. (2018), “Predicting Future Instance Segmentations by Forecasting Convolutional Features”, European Conference on Computer Vision (ECCV 2018)

Z. Yang, J. Zhao, B. Dhingra, K. He, W. W. Cohen, R. R. Salakhutdinov, and Y. LeCun. (2018), “Glomo: Unsupervised learning of transferable relational graphs”, Advances in Neural Information Processing Systems, Vol. 31

Y. LeCun. (2018), “The Power and Limits of Deep Learning: In his IRI Medal address, Yann LeCun maps the development of machine learning techniques and suggests what the future may hold.”, Research-Technology Management, Vol. 61, pages 22-27

A. Ramesh, Y. Choi, and Y. LeCun. (2018), “A spectral regularizer for unsupervised disentanglement”, arXiv preprint arXiv:1812.01161

Y. LeCun. (2018), “Learning world models: The next step towards AI”, Keynote, the 27th IJCAI

L. Yann, C. Corinna, and J. Christopher. (2018), “The MNIST database of handwritten digits. 1998”, URL http://yann. lecun. com/exdb/mnist

A. Ramesh, and Y. LeCun. (2018), “Backpropagation for implicit spectral densities”, arXiv preprint arXiv:1806.00499

X. Zhang, and Y. LeCun. (2018), “Byte-level recursive convolutional auto-encoder for text”, arXiv preprint arXiv:1802.01817

Y. LeCun, and M. Ford. (2018), “Yann LeCun”, Architects of Intelligence. Packt Publishing, Birmingham

M. Bronstein, X. Bresson, A. Szlam, J. Bruna, and Y. LeCun. (2018), “Tutorial: Geometric deep learning on graphs and manifolds”, Society for Industrial and Applied Mathematics (SIAM)

X. Zhang, and Y. LeCun. (2018), “Adversarially-Trained Normalized Noisy-Feature Auto-Encoder for Text Generation”, arXiv preprint arXiv:1811.04201

Y. LeCun. (2018), “Informatique et sciences numériques”, L’annuaire du Collège de France. Cours et travaux, pages 567-568

Y. LeCun. (2018), “Advancing AI: From Academia to Facebook and Back”, 2018 AAAS Annual Meeting

2017

B. M. Lake, T. D. Ullman, J. B. Tenenbaum, and S. J. Gershman. (2017), “Building machines that learn and think like people”, Behavioral and Brain Sciences, Vol. 40

A. Rothe, B. M. Lake, and T. M. Gureckis. (2017), “Question asking as program generation”, Advances in Neural Information Processing Systems 30

B. M. Lake, T. D. Ullman, J. B. Tenenbaum, and S. J. Gershman. (2017), “Ingredients of intelligence: From classic debates to an engineering roadmap”, Behavioral and Brain Sciences, Vol. 40

B. Lake. (2017), “Finding solace in defeat by artificial intelligence”, MIT Technology Review

Y. Jernite, S. R. Bowman, and D. Sontag. (2017), “Discourse-based objectives for fast unsupervised sentence representation learning”, arXiv preprint 1705.00557

N. Nangia, A. Williams, A. Lazaridou, and S. R. Bowman. (2017), “The RepEval 2017 shared task: Multi-genre natural language inference with sentence representations”, Proceedings of RepEval 2017

R. Kshirsagar, R. Morris, and S. R. Bowman. (2017), “Detecting and explaining crisis”, Proceedings of CLPsych

S. Brarda, P. Yeres, and S. R. Bowman. (2017), “Sequential Attention: A Context-Aware Alignment Function for Machine Reading”, Proceedings of the Second Workshop on Representation Learning for NLP (RepL4NLP)

V. Dhar, and S. Bowman. (2017), “A Perspective on Natural Language Understanding Capability: An Interview with Sam Bowman”, Big Data, Vol. 5, pages 5–11

S. Bowman, Y. Goldberg, F. Hill, A. Lazaridou, O. Levy, R. Reichart, and A. Søgaard. (2017), “Proceedings of the 2nd Workshop on Evaluating Vector Space Representations for NLP”, Proceedings of the 2nd Workshop on Evaluating Vector Space Representations for NLP

C. Gulcehre, O. Firat, K. Xu, K. Cho, and Y. Bengio. (2017), “On integrating a language model into neural machine translation”, Computer Speech & Language, Vol. 45, pages 137-148

K. Choi, G. Fazekas, M. Sandler, and K. Cho. (2017), “Convolutional recurrent neural networks for music classification”, 2017 IEEE International conference on acoustics, speech and signal processing (ICASSP), pages 2392-2396

J. Lee, K. Cho, and T. Hofmann. (2017), “Fully Character-Level Neural Machine Translation without Explicit Segmentation”, Transactions of the Association for Computational Linguistics (TACL)

R. Sennrich, O. Firat, K. Cho, A. Birch, B. Haddow, J. Hitschler, M. Junczys-Dowmunt, S. Läubli, A. Barone, J. Mokry, and M. Nădejde. (2017), “Nematus: a toolkit for neural machine translation”, EACL, pages 65-68

M. Dunn, L. Sagun, M. Higgins, V. U. Guney, V. Cirik, and K. Cho. (2017), “SearchQA: A new q&a dataset augmented with context from a search engine”, arXiv preprint arXiv:1704.05179

J. Zhang, and K. Cho. (2017), “Query-Efficient Imitation Learning for End-to-End Autonomous Driving”, AAAI

K. Choi, G. Fazekas, M. Sandler, and K. Cho. (2017), “Transfer learning for music classification and regression tasks”, ISMIR, pages 141-149

K. J. Geras, S. Wolfson, Y. Shen, N. Wu, S. Kim, E. Kim, L. Heacock, U. Parikh, L. Moy, and K. Cho. (2017), “High-resolution breast cancer screening with multi-view deep convolutional neural networks”, arXiv preprint arXiv:1703.07047

R. Nogueira, and K. Cho. (2017), “Task-oriented query reformulation with reinforcement learning”, EMNLP, pages 574-583

A. Eriguchi, Y. Tsuruoka, and K. Cho. (2017), “Learning to parse and translate improves neural machine translation”, ACL, pages 72-78

S. Jean, S. Lauly, O. Firat, and K. Cho. (2017), “Does neural machine translation benefit from larger context?”, arXiv preprint arXiv:1704.05135

F. Hill, K. Cho, S. Jean, and Y. Bengio. (2017), “The representational geometry of word meanings acquired by neural machine translation models”, Machine Translation, Vol. 31, pages 3-18

H. Choi, K. Cho, and Y. Bengio. (2017), “Context-dependent word representation for neural machine translation”, Computer Speech & Language, Vol. 45, pages 149-160

K. Evtimova, A. Drozdov, D. Kiela, and K. Cho. (2017), “Emergent communication in a multi-modal, multi-step referential game”, ICLR

J. Gu, K. Cho, and V. O. Li. (2017), “Trainable greedy decoding for neural machine translation”, EMNLP, pages 1968-1978

J. Lee, K. Cho, J. Weston, and D. Kiela. (2017), “Emergent translation in multi-agent communication”, ICLR

K. Evtimova, A. Drozdov, D. Kiela, and K. Cho. (2017), “Emergent language in a multi-modal, multi-step referential game”, arXiv preprint arXiv:1705.10369, Vol. 3

M. Chen, Z. Lin, and K. Cho. (2017), “Graph convolutional networks for classification with a structured label space”, arXiv preprint arXiv:1710.04908

S. Welleck, J. Mao, K. Cho, and Z. Zhang. (2017), “Saliency-based sequential image attention with multiset prediction”, Advances in neural information processing systems, Vol. 30

S. Jean, S. Lauly, O. Firat, and K. Cho. (2017), “Neural machine translation for cross-lingual pronoun prediction”, Proceedings of the third workshop on discourse in machine translation, pages 54-57

P. Blunsom, K. Cho, C. Dyer, and H. Schütze. (2017), “From characters to understanding natural language (C2NLU): Robust end-to-end deep learning for NLP (Dagstuhl Seminar 17042)”, Dagstuhl Reports, Vol. 7

M. Dunn, L. Sagun, M. Higgins, V. U. Güney, V. Cirik, and K. Cho. (2017), “SearchQA: a new Q&A dataset augmented with context from a search engine. CoRR abs/1704.05179 (2017)”, arXiv preprint arXiv:1704.05179

K. Choi, G. Fazekas, K. Cho, and M. Sandler. (2017), “The effects of noisy labels on deep convolutional neural networks for music classification”, arXiv preprint arXiv:1706.02361

R. D. Hjelm, A. P. Jacob, T. Che, A. Trischler, K. Cho, and Y. Bengio. (2017), “Boundary-Seeking Generative Adversarial Networks”, ICAISC, pages 73-84

S. Hallyburton, G. Chang, S. Honig, K. Cho, and C. M. Deniz. (2017), “Automatic segmentation of mr images of the proximal femur using deep learning”, Proceedings 25th Scientific Meeting, ISMRM, Hawaii, Vol. 3986

K. Cho. (2017), “Strawman: an Ensemble of Deep Bag-of-Ngrams for Sentiment Analysis”, arXiv preprint arXiv:1707.08939

P. Blunsom, A. Bordes, K. Cho, S. B. Cohen, C. Dyer, E. Grefenstette, K. M. Hermann, L. Rimell, J. Weston, and S. Yih. (2017), “Proceedings of the 2nd Workshop on Representation Learning for NLP”, Proceedings of the 2nd Workshop on Representation Learning for NLP

I. Guyon, U. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett. (2017), “Curran Associates”, Inc.: Red Hook, NY, USA, Vol. 30, pages 3149-3157

S. Sukhbaatar, Z. Lin, I. Kostrikov, G. Synnaeve, A. Szlam, and R. Fergus. (2017), “Intrinsic motivation and automatic curricula via asymmetric self-play”, ICLR

K. Schütt, P. Kindermans, H. Sauceda, S. Chmiela, A. Tkatchenko, K. Müller, I. Guyon, U. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett. (2017), “Advances in Neural Information Processing Systems 30”, Guyon, I., Luxburg, UV, Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R., Eds, pages 991-1001

R. Nilsson, A. Veicht, P. G. Godfrey, E. Rice, J. Aguilar, L. Pueyo, L. Roberts, R. Oppenheimer, D. Brenner, S. Luszcz-Cook, E. Bacchus, C. Beichman, R. Burruss, E. Cady, R. Dekany, R. Fergus, L. Hillenbrand, S. Hinkley, D. King, T. Lockhart, I. Parry, A. Sivaramakrishnan, R. Soummer, G. Vasisht, C. Zhai, and N. Zimmerman. (2017), “Project 1640 Observations of Brown Dwarf GJ 758 B: Near-infrared Spectrum and Atmospheric Modeling”, The Astrophysical Journal, Vol. 838, pages 64

U. V. Luxburg, I. Guyon, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett. (2017), “Advances in neural information processing systems 30”, 31st annual conference on neural information processing systems (NIPS 2017), Long Beach, California, USA, pages 4-9

M. M. Bronstein, J. Bruna, Y. LeCun, A. Szlam, and P. Vandergheynst. (2017), “Geometric deep learning: going beyond euclidean data”, IEEE Signal Processing Magazine, Vol. 34, pages 18-42

V. Garcia, and J. Bruna. (2017), “Few-shot learning with graph neural networks”, Neural Networks, pages 94-105

J. Bruna, and X. Li. (2017), “Community detection with graph neural networks”, stat, Vol. 1050, pages 27

R. Vidal, J. Bruna, R. Giryes, and S. Soatto. (2017), “Mathematics of deep learning”, arXiv preprint arXiv:1712.04741

I. Dokmanic, J. Bruna, S. Mallat, and M. d. Hoop. (2017), “Invariant multiscale statistics for inverse problems”, Proc. Signal Processing with Adaptive Sparse Structured Representations (SPARS) workshop, pages 5-8

M. Brinstein, J. Bruna, X. Bresson, and Y. LeCun. (2017), “Geometric deep learning on graphs and manifolds”, Proceedings of Conference on Neural Information Processing Systems, Los Angeles, California, USA

V. Garcia, and J. Bruna. (2017), “FEW-SHOT LEARNING WITH GRAPH NEURAL NET”, stat, Vol. 1050, pages 10

M. M. Bronstein, J. Bruna, Y. LeCun, A. Szlam, and P. Vandergheynst. (2017), “Geometric deep learning: going beyond euclidean data”, IEEE Signal Processing Magazine, Vol. 34, pages 18-42

D. Tran, H. Wang, L. Torresani, J. Ray, Y. LeCun, and M. Paluri. (2017), “A Closer Look at Spatiotemporal Convolutions for Action Recognition”, computer vision and pattern recognition conference (CVPR 2018)

P. Luc, N. Neverova, C. Couprie, J. Verbeek, and Y. LeCun. (2017), “Predicting Deeper into the Future of Semantic Segmentation”, International Conference on Computer Vision (ICCV 2017)

Y. Kim, K. Zhang, A. M. Rush, and Y. LeCun. (2017), “Adversarially regularized autoencoders for generating discrete structures”, arXiv preprint arXiv:1706.04223, Vol. 2, pages 12

X. Zhang, and Y. LeCun. (2017), “Which encoding is the best for text classification in chinese, english, japanese and korean?”, arXiv preprint arXiv:1708.02657

M. Henaff, W. F. Whitney, and Y. LeCun. (2017), “Model-based planning with discrete and continuous actions”, arXiv preprint arXiv:1705.07177

X. Zhang, and Y. LeCun. (2017), “Universum prescription: Regularization using unlabeled data”, Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 31

M. Henaff, J. Zhao, and Y. LeCun. (2017), “Prediction under uncertainty with error-encoding networks”, arXiv preprint arXiv:1711.04994

C. Wu, M. Tygert, and Y. LeCun. (2017), “Hierarchical loss for classification”, IJCNN, pages 1-7

M. Henaff, W. F. Whitney, and Y. LeCun. (2017), “Model-based planning in discrete action spaces”, arXiv preprint arXiv:1705.07177, Vol. 2

Y. LeCun. (2017), “My take on Ali Rahimi’s ‘Test of Time’award talk at NIPS”, Facebook, https://www. facebook. com/yann. lecun/posts/101 54938130592143, pages 06-12

Y. LeCun. (2017), “Qu’est-ce que l’intelligence artificielle”, chaire Recherches sur l’intelligence artificielle, informatique et sciences numériques (2015-2016), Collège de France,< https://www. college-de-france. fr/media/yannlecun/UPL4485925235409209505_Intelligence_Artificielle______Y. _LeCun. pdf

2016

S. R. Bowman, L. Vilnis, O. Vinyals, A. M. Dai, R. Jozefowicz, and S. Bengio. (2016), “Generating sentences from a continuous space”, Proceedings of CoNLL

S. R. Bowman, J. Gauthier, A. Rastogi, R. Gupta, C. D. Manning, and C. Potts. (2016), “A Fast Unified Model for Parsing and Sentence Understanding”, Proceedings of ACL

R. Al-Rfou, G. Alain, A. Almahairi, C. Angermueller, D. Bahdanau, N. Ballas, F. Bastien, J. Bayer, A. Belikov, A. Belopolsky, Y. Bengio, A. Bergeron, J. Bergstra, V. Bisson, J. B. Snyder, N. Bouchard, N. Boulanger-Lewandowski, X. Bouthillier, A. d. Brébisson, O. Breuleux, P. Carrier, K. Cho, J. Chorowski, P. Christiano, T. Cooijmans, M. Côté, M. Côté, A. Courville, Y. N. Dauphin, O. Delalleau, J. Demouth, G. Desjardins, S. Dieleman, L. Dinh, M. Ducoffe, V. Dumoulin, S. E. Kahou, D. Erhan, Z. Fan, O. Firat, M. Germain, X. Glorot, I. Goodfellow, M. Graham, C. Gulcehre, P. Hamel, I. Harlouchet, J. Heng, B. Hidasi, S. Honari, A. Jain, S. Jean, K. Jia, M. Korobov, V. Kulkarni, A. Lamb, P. Lamblin, E. Larsen, C. Laurent, S. Lee, S. Lefrancois, S. Lemieux, N. Léonard, Z. Lin, J. A. Livezey, C. Lorenz, J. Lowin, Q. Ma, P. Manzagol, O. Mastropietro, R. T. McGibbon, R. Memisevic, B. v. Merriënboer, V. Michalski, M. Mirza, A. Orlandi, C. Pal, R. Pascanu, M. Pezeshki, C. Raffel, D. Renshaw, M. Rocklin, A. Romero, M. Roth, P. Sadowski, J. Salvatier, F. Savard, J. Schlüter, J. Schulman, G. Schwartz, I. V. Serban, D. Serdyuk, S. Shabanian, É. Simon, S. Spieckermann, S. R. Subramanyam, J. Sygnowski, J. Tanguay, G. v. Tulder, J. Turian, S. Urban, P. Vincent, F. Visin, H. d. Vries, D. Warde-Farley, D. J. Webb, M. Willson, K. Xu, L. Xue, L. Yao, S. Zhang, and Y. Zhang. (2016), “Theano: A Python framework for fast computation of mathematical expressions”, arXiv e-prints, pages arXiv: 1605.02688

O. Firat, K. Cho, and Y. Bengio. (2016), “Multi-Way, Multilingual Neural Machine Translation with a Shared Attention Mechanism”, NAACL

T. H. Nguyen, K. Cho, and R. Grishman. (2016), “Joint event extraction via recurrent neural networks”, Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: human language technologies, pages 300-309

F. Hill, K. Cho, and A. Korhonen. (2016), “Learning distributed representations of sentences from unlabelled data”, NAACL

J. Chung, K. Cho, and Y. Bengio. (2016), “A Character-level Decoder without Explicit Segmentation for Neural Machine Translation”, ACL

F. Visin, M. Ciccone, A. Romero, K. Kastner, K. Cho, Y. Bengio, M. Matteucci, and A. Courville. (2016), “Reseg: A recurrent neural network-based model for semantic segmentation”, Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pages 41-48

Y. Xiao, and K. Cho. (2016), “Efficient character-level document classification by combining convolution and recurrent layers”, arXiv preprint arXiv:1602.00367

O. Firat, B. Sankaran, Y. Al-Onaizan, F. Vural, and K. Cho. (2016), “Zero-Resource Translation with Multi-Lingual Neural Machine Translation”, EMNLP

F. Hill, K. Cho, A. Korhonen, and Y. Bengio. (2016), “Learning to understand phrases by embedding the dictionary”, TACL

J. Gu, G. Neubig, K. Cho, and V. O. Li. (2016), “Learning to translate in real-time with neural machine translation”, EACL, pages 1053-1062

K. Cho, and M. Esipova. (2016), “Can neural machine translation do simultaneous translation?”, arXiv preprint arXiv:1606.02012

T. Wang, and K. Cho. (2016), “Larger-Context Language Modelling”, ACL

Y. Miyamoto, and K. Cho. (2016), “Gated Word-Character Recurrent Language Model”, EMNLP

C. Gulcehre, S. Chandar, K. Cho, and Y. Bengio. (2016), “Dynamic neural turing machine with soft and hard addressing schemes”, arXiv preprint arXiv:1607.00036

A. Almahairi, K. Cho, N. Habash, and A. Courville. (2016), “First result on Arabic neural machine translation”, arXiv preprint arXiv:1606.02680

K. Cho. (2016), “Noisy parallel approximate decoding for conditional recurrent language model”, arXiv preprint arXiv:1605.03835

T. H. Nguyen, L. Fu, K. Cho, and R. Grishman. (2016), “A two-stage approach for extending event detection to new types via neural networks”, Proceedings of the 1st Workshop on Representation Learning for NLP, pages 158-165

R. Nogueira, and K. Cho. (2016), “End-to-End Goal-Driven Web Navigation”, NIPS

D. Hjelm, R. R. Salakhutdinov, K. Cho, N. Jojic, V. Calhoun, and J. Chung. (2016), “Iterative refinement of the approximate posterior for directed belief networks”, Advances in neural information processing systems, Vol. 29

A. Saha, M. M. Khapra, S. Chandar, J. Rajendran, and K. Cho. (2016), “A correlational encoder decoder architecture for pivot based sequence generation”, COLING, pages 109-118

K. Choi, G. Fazekas, B. McFee, K. Cho, and M. Sandler. (2016), “Towards music captioning: Generating music playlist descriptions”, arXiv preprint arXiv:1608.04868

J. Chung, K. Cho, and Y. Bengio. (2016), “NYU-MILA Neural Machine Translation Systems for WMT16”, Proceedings of the First Conference on Machine Translation, Berlin, Germany. Association for Computational Linguistics

R. D. Hjelm, K. Cho, J. Chung, R. Salakhutdinov, V. Calhoun, and N. Jojic. (2016), “Iterative refinement of approximate posterior for training directed belief networks”, Neuron Information Processing Systems, in press

M. F. Amin, S. M. Plis, E. Damaraju, D. Hjelm, K. Cho, and V. D. Calhoun. (2016), “Multimodal fusion of brain structural and functional imaging with a deep neural machine translation approach”, 2016 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI), pages 1-4

H. Kim, S. Hwang, and K. Cho. (2016), “Semantic Noise Modeling for Better Representation Learning”, arXiv preprint arXiv:1611.01268

R. D. Hjelm, E. Damaraju, K. Cho, H. Laufs, S. M. Plis, and V. Calhoun. (2016), “Spatio-temporal Dynamics of Intrinsic Networks in Functional Magnetic Imaging Data Using Recurrent Neural Networks”, arXiv e-prints, pages arXiv: 1611.00864

P. Blunsom, K. Cho, S. B. Cohen, E. Grefenstette, K. M. Hermann, L. Rimell, J. Weston, and W. Yih. (2016), “Proceedings of the 1st Workshop on Representation Learning for NLP”, Proceedings of the 1st Workshop on Representation Learning for NLP

S. Sukhbaatar, and R. Fergus. (2016), “Learning multiagent communication with backpropagation”, Advances in neural information processing systems, Vol. 29

A. Lerer, S. Gross, and R. Fergus. (2016), “Learning physical intuition of block towers by example”, International conference on machine learning, pages 430-438

E. Denton, S. Gross, and R. Fergus. (2016), “Semi-supervised learning with context-conditional generative adversarial networks”, arXiv preprint arXiv:1611.06430

D. Tran, L. Bourdev, R. Fergus, L. Torresani, and M. Paluri. (2016), “Deep end2end voxel2voxel prediction”, Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pages 17-24

W. Zaremba, T. Mikolov, A. Joulin, and R. Fergus. (2016), “Learning simple algorithms from examples”, International conference on machine learning, pages 421-429

C. D. Freeman, and J. Bruna. (2016), “Topology and geometry of half-rectified network optimization”, ICLR

M. Tygert, J. Bruna, S. Chintala, Y. LeCun, S. Piantino, and A. Szlam. (2016), “A mathematical motivation for complex-valued convolutional networks”, Neural computation, Vol. 28, pages 815-825

T. Moreau, and J. Bruna. (2016), “Understanding trainable sparse coding via matrix factorization”, arXiv preprint arXiv:1609.00285

S. Mobin, and J. Bruna. (2016), “Voice conversion using convolutional neural networks”, arXiv preprint arXiv:1610.08927

J. Bruna. (2016), “Talk 1: Convolutional neural networks against the curse of dimensionality”, Book of abstracts, pages 40-42

A. Conneau, H. Schwenk, L. Barrault, and Y. Lecun. (2016), “Very deep convolutional networks for natural language processing”, arXiv preprint arXiv:1606.01781, Vol. 2

J. Zhao, M. Mathieu, and Y. LeCun. (2016), “Energy-based generative adversarial network”, international Conference on Learning Representations (ICLR 2017)

J. Žbontar, and Y. LeCun. (2016), “Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches”, Journal of Machine Learning Research, Vol. 17, pages 1-32

P. Chaudhari, A. Choromanska, S. Soatto, Y. LeCun, C. Baldassi, C. Borgs, J. Chayes, L. Sagun, and R. Zecchina. (2016), “Entropy-sgd: Biasing gradient descent into wide valleys”, international Conference on Learning Representations (ICLR 2017)

M. F. Mathieu, J. J. Zhao, J. Zhao, A. Ramesh, P. Sprechmann, and Y. LeCun. (2016), “Disentangling factors of variation in deep representation using adversarial training”, Advances in neural information processing systems, Vol. 29

M. Henaff, J. Weston, A. Szlam, A. Bordes, and Y. LeCun. (2016), “Tracking the World State with Recurrent Entity Networks”, international Conference on Learning Representations (ICLR 2017)

T. Sercu, C. Puhrsch, B. Kingsbury, and Y. LeCun. (2016), “Very deep multilingual convolutional neural networks for LVCSR”, 2016 IEEE international conference on acoustics, speech and signal processing (ICASSP), pages 4955-4959

L. Jing, Y. Shen, T. Dubček, J. Peurifoy, S. Skirlo, Y. LeCun, M. Tegmark, and M. Soljačić. (2016), “Tunable efficient unitary neural networks (EUNN) and their application to RNNs”, International Conference on Machine Learning (ICML 2017)

M. Henaff, A. Szlam, and Y. LeCun. (2016), “Orthogonal RNNs and Long-Memory Tasks”, international Conference on Machine Learning (ICML 2016)

M. Tygert, J. Bruna, S. Chintala, Y. LeCun, S. Piantino, and A. Szlam. (2016), “A Mathematical Motivation for Complex-Valued Convolutional Networks”, Neural computation, Vol. 28, pages 815-825

L. Sagun, L. Bottou, and Y. LeCun. (2016), “Singularity of the hessian in deep learning”, arXiv preprint arXiv:1611.07476, Vol. 54

Y. LeCun. (2016), “L’apprentissage profond, une révolution en intelligence artificielle”, La lettre du Collège de France, pages 13

Y. LeCun. (2016), “Les enjeux de la recherche en intelligence artificielle”, Accès https://dataanalyticspost. com/wp-content/uploads/2017/04/ylecun_college_France. pdf

A. Provodin, L. Torabi, B. Flepp, Y. LeCun, M. Sergio, L. D. Jackel, U. Muller, and J. Zbontar. (2016), “Fast incremental learning for off-road robot navigation”, arXiv preprint arXiv:1606.08057

P. J. Dugan, C. W. Clark, Y. A. LeCun, and S. Parijs. (2016), “Phase 4: Dcl system using deep learning approaches for land-based or ship-based real-time recognition and localization of marine mammals-distributed processing and big data applications”, arXiv preprint arXiv:1605.00982

Y. Jernite, A. Choromanska, D. Sontag, and Y. LeCun. (2016), “Simultaneous learning of trees and representations for extreme classification, with application to language modeling”, arXiv preprint arXiv:1610.04658

Y. LeCun. (2016), “L’apprentissage profond”, Lectures at Collège de France

Y. LeCun. (2016), “Unsupervised learning”, Unsupervised learning

P. J. Dugan, C. W. Clark, Y. A. LeCun, and S. Parijs. (2016), “Phase 1: DCL System Research Using Advanced Approaches for Land-based or Ship-based Real-Time Recognition and Localization of Marine Mammals-HPC System Implementation”, arXiv preprint arXiv:1605.00971

K. Jarrett, K. Kvukcuoglu, K. Gregor, and Y. LeCun. (2016), “What is the Best Feature Learning Procedure in Hierarchical Recognition Architectures?”, arXiv preprint arXiv:1606.01535

J. Zhao, M. Mathieu, and Y. LeCun. (2016), “ENERGY-BASED GENERATIVE ADVERSARIAL NET”, IEEE Access, pages 5397-5411

2015

K. Xu, J. Ba, R. Kiros, K. Cho, A. Courville, R. Salakhutdinov, R. Zemel, and Y. Bengio. (2015), “Show, Attend and Tell: Neural Image Caption Generation with Visual Attention”, International Conference on Machine Learning

J. Chorowski, D. Bahdanau, D. Serdyuk, K. Cho, and Y. Bengio. (2015), “Attention-based models for speech recognition”, The Twenty-ninth Annual Conference on Neural Information Processing Systems (NIPS)

L. Yao, A. Torabi, K. Cho, N. Ballas, C. Pal, H. Larochelle, and A. Courville. (2015), “Describing videos by exploiting temporal structure”, International Conference on Computer Vision

J. Chung, C. Gulcehre, K. Cho, and Y. Bengio. (2015), “Gated Feedback Recurrent Neural Networks”, International Conference on Machine Learning

K. Cho, A. Courville, and Y. Bengio. (2015), “Describing multimedia content using attention-based encoder-decoder networks”, IEEE Transactions on Multimedia, Vol. 17, pages 1875-1886

F. Visin, K. Kastner, K. Cho, M. Matteucci, A. Courville, and Y. Bengio. (2015), “Renet: A recurrent neural network based alternative to convolutional networks”, arXiv preprint arXiv:1505.00393

S. Jean, O. Firat, K. Cho, R. Memisevic, and Y. Bengio. (2015), “Montreal neural machine translation systems for WMT’15”, Proceedings of the tenth workshop on statistical machine translation, pages 134-140

L. Lu, X. Zhang, K. Cho, and S. Renals. (2015), “A Study of the Recurrent Neural Network Encoder-Decoder for Large Vocabulary Speech Recognition”, INTERSPEECH

K. Cho. (2015), “Natural language understanding with distributed representation”, arXiv preprint arXiv:1511.07916

Q. Gan, Q. Guo, Z. Zhang, and K. Cho. (2015), “First step toward model-free, anonymous object tracking with recurrent neural networks”, arXiv preprint arXiv:1511.06425

H. Schulz, K. Cho, T. Raiko, and S. Behnke. (2015), “Two-layer contractive encodings for learning stable nonlinear features”, Neural Networks, Vol. 64, pages 4-11

M. Berglund, T. Raiko, and K. Cho. (2015), “Measuring the usefulness of hidden units in Boltzmann machines with mutual information”, Neural Networks, Vol. 64, pages 12-18

L. Yao, N. Ballas, K. Cho, J. R. Smith, and Y. Bengio. (2015), “Oracle performance for visual captioning”, BMVC

K. Cho. (2015), “Introduction to neural machine translation with GPUs (part 3)”, URL: https://devblogs. nvidia. com/p arallelforall/introduction-neural-machine-translation-with-gpus

F. Amin, S. Plis, E. Damaraju, D. Hjelm, K. Cho, and V. Calhoun. (2015), “A deep-learning approach to translate between brain structure and brain function”, Pattern Recognition in NeuroImaging (PRNI)

K. Cho, T. Raiko, A. Ilin, and J. Karhunen. (2015), “How to pretrain deep Boltzmann machines in two stages”, Artificial Neural Networks: Methods and Applications in Bio-/Neuroinformatics, pages 201-219

M. Moczulski, K. Xu, A. Courville, and K. Cho. (2015), “A Controller-Recognizer Framework: How necessary is recognition for control?”, arXiv preprint arXiv:1511.06428

K. Xu, J. Ba, R. Kiros, K. Cho, A. Courville, R. Salakhutdinov, R. Zemel, and Y. Bengio. (2015), “Show, Attend and Tell”, Neural image caption generation with visual attention

R. D. Hjelm, K. Cho, J. Chung, R. Salakhutdinov, V. Calhoun, and N. Jojic. (2015), “Iterative Refinement of the Approximate Posterior for Directed Belief Networks”, NIPS, pages 4691-4699

D. Tran, L. Bourdev, R. Fergus, L. Torresani, and M. Paluri. (2015), “Learning spatiotemporal features with 3d convolutional networks”, Proceedings of the IEEE international conference on computer vision, pages 4489-4497

D. Eigen, and R. Fergus. (2015), “Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture”, Proceedings of the IEEE international conference on computer vision, pages 2650-2658

S. Sukhbaatar, J. Weston, and R. Fergus. (2015), “End-to-end memory networks”, Advances in neural information processing systems, Vol. 28

E. L. Denton, S. Chintala, and R. Fergus. (2015), “Deep generative image models using a laplacian pyramid of adversarial networks”, Advances in neural information processing systems, Vol. 28

B. Zhou, Y. Tian, S. Sukhbaatar, A. Szlam, and R. Fergus. (2015), “Simple baseline for visual question answering”, arXiv preprint arXiv:1512.02167

N. Zhang, M. Paluri, Y. Taigman, R. Fergus, and L. Bourdev. (2015), “Beyond frontal faces: Improving person recognition using multiple cues”, Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4804-4813

K. Tang, M. Paluri, L. Fei-Fei, R. Fergus, and L. Bourdev. (2015), “Improving image classification with location context”, Proceedings of the IEEE international conference on computer vision, pages 1008-1016

S. Sukhbaatar, A. Szlam, J. Weston, and R. Fergus. (2015), “Weakly supervised memory networks”, CoRR, abs/1503.08895, Vol. 2

Y. Gong, M. Pawlowski, F. Yang, L. Brandy, L. Bourdev, and R. Fergus. (2015), “Web scale photo hash clustering on a single machine”, Proceedings of the IEEE conference on computer vision and pattern recognition, pages 19-27

L. Wan, D. Eigen, and R. Fergus. (2015), “End-to-end integration of a convolution network, deformable parts model and non-maximum suppression”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 851-859

B. M. Lake, W. Zaremba, R. Fergus, and T. M. Gureckis. (2015), “Deep Neural Networks Predict Category Typicality Ratings for Images.”, CogSci

S. Sukhbaatar, A. Szlam, G. Synnaeve, S. Chintala, and R. Fergus. (2015), “Mazebase: A sandbox for learning from games”, arXiv preprint arXiv:1511.07401

U. Samadani, S. Farooq, R. Ritlop, F. Warren, M. Reyes, E. Lamm, A. Alex, E. Nehrbass, R. Kolecki, M. Jureller, J. Schneider, A. Chen, C. Shi, N. Mendhiratta, J. H. Huang, M. Qian, R. Kwak, A. Mikheev, H. Rusinek, A. George, R. Fergus, D. Kondziolka, P. P. Huang, and R. T. Smith. (2015), “Detection of third and sixth cranial nerve palsies with a novel method for eye tracking while watching a short film clip”, Journal of neurosurgery, Vol. 122, pages 707-720

X. Zhang, J. Zhao, and Y. LeCun. (2015), “Character-level convolutional networks for text classification”, Advances in neural information processing systems, Vol. 28

M. Mathieu, C. Couprie, and Y. LeCun. (2015), “Deep multi-scale video prediction beyond mean square error”, international Conference on Learning Representations (ICLR 2016)

M. Henaff, J. Bruna, and Y. LeCun. (2015), “Deep convolutional networks on graph-structured data”, arXiv preprint arXiv:1506.05163

A. Choromanska, M. Henaff, M. Mathieu, G. B. Arous, and Y. LeCun. (2015), “The Loss Surface of Multilayer Networks”, AI & Statistics (AIStats 2015)

Y. LeCun. (2015), “LeNet-5, convolutional neural networks”, URL: http://yann. lecun. com/exdb/lenet, Vol. 20, pages 14

X. Zhang, and Y. LeCun. (2015), “Text understanding from scratch”, arXiv preprint arXiv:1502.01710

S. Zhang, A. E. Choromanska, and Y. LeCun. (2015), “Deep learning with elastic averaging SGD”, Advances in neural information processing systems, Vol. 28

D. P. Kingma, J. Ba, Y. Bengio, and Y. LeCun. (2015), “3rd International Conference on Learning Representations”, ICLR, San Diego

J. Bruna, P. Sprechmann, and Y. LeCun. (2015), “Super-Resolution with Deep Convolutional Sufficient Statistics”, International Conference on Learning Representations (ICLR 2016)

J. Zhao, M. Mathieu, R. Goroshin, and Y. Lecun. (2015), “Stacked what-where auto-encoders”, arXiv preprint arXiv:1506.02351

R. Goroshin, J. Bruna, J. Tompson, D. Eigen, and Y. LeCun. (2015), “Unsupervised learning of spatiotemporally coherent metrics”, Proceedings of the IEEE international conference on computer vision, pages 4086-4093

Y. LeCun, and Y. Bengio. (2015), “GJ n”, Hinton,” Deep learning, Vol. 521, pages 436-444

R. Goroshin, M. F. Mathieu, and Y. LeCun. (2015), “Learning to linearize under uncertainty”, Advances in neural information processing systems, Vol. 28

A. Choromanska, Y. LeCun, and G. B. Arous. (2015), “Open problem: The landscape of the loss surfaces of multilayer networks”, Conference on Learning Theory, pages 1756-1760

K. Simonyan, A. Zisserman, Y. Bengio, and Y. LeCun. (2015), “3rd International Conference on Learning Representations”, ICLR, San Diego

Y. LeCun. (2015), “Deep learning & convolutional networks.”, Hot Chips Symposium, pages 1-95

D. P. Kingma, J. Ba, Y. Bengio, and Y. LeCun. (2015), “3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings”, ICLR

Y. LeCun, Y. Bengio, and G. Hinton. (2015), “Nature”, Nature, Vol. 521, pages 436

R. Goroshin, J. Bruna, J. Tompson, D. Eigen, and Y. LeCun. (2015), “Unsupervised feature learning from temporal data”, arXiv preprint arXiv:1504.02518

A. Choromanska, K. Choromanski, M. Bojarski, T. Jebara, S. Kumar, and Y. LeCun. (2015), “Binary embeddings with structured hashed projections”, International Conference on Machine Learning (ICML 2016)

Y. Lecun, and Y. Bengio. (2015), “et HINTON, Geoffrey”, Deep learning. nature, Vol. 521, pages 436

J. Bruna, S. Chintala, Y. LeCun, S. Piantino, A. Szlam, and M. Tygert. (2015), “A theoretical argument for complex-valued convolutional networks”, arXiv preprint arXiv:1503.03438, Vol. 139

M. Ranzato, G. Hinton, and Y. LeCun. (2015), “Guest editorial: Deep learning”, International Journal of Computer Vision, Vol. 113, pages 1-2

Y. LeCun. (2015), “What’s wrong with deep learning”, CVPR, keynote

L. Sagun, T. Trogdon, and Y. LeCun. (2015), “Universal halting times in optimization and machine learning”, arXiv preprint arXiv:1511.06444

J. Bruna, P. Sprechmann, and Y. LeCun. (2015), “Source separation with scattering non-negative matrix factorization”, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1876-1880

P. Dugan, J. Zollweg, M. Popescu, D. Risch, H. Glotin, and Y. LeCun. (2015), “High performance computer acoustic data accelerator: A new system for exploring marine mammal acoustics for big data applications”, arXiv preprint arXiv:1509.03591

P. Sprechmann, J. Bruna, and Y. LeCun. (2015), “Audio source separation with discriminative scattering networks”, Latent Variable Analysis and Signal Separation: 12th International Conference, LVA/ICA 2015, Liberec, Czech Republic, August 25-28, 2015, Proceedings 12, pages 259-267

P. J. Dugan, C. W. Clark, Y. A. LeCun, and S. Parijs. (2015), “DCL System Using Deep Learning Approaches for Land-Based or Ship-Based Real Time Recognition and Localization of Marine Mammals. Bioacoustics Research Program, Cornell University Ithaca United States”, Bioacoustics Research Program, Cornell University: Ithaca, NY, USA

R. Marc’Aurelio’A, G. Hinton, and Y. LeCun. (2015), “Guest editorial: Deep learning”, International Journal of Computer Vision, Vol. 113, pages 1-2

S. Zhang, A. Choromanska, and Y. LeCun. (2015), “Deep learning with elastic averaging SGD: 3rd International Conference on Learning Representations, ICLR 2015”, 3rd International Conference on Learning Representations, ICLR 2015

2014

M. D. Zeiler, and R. Fergus. (2014), “Visualizing and understanding convolutional networks”, Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part I 13, pages 818-833

D. Eigen, C. Puhrsch, and R. Fergus. (2014), “Depth map prediction from a single image using a multi-scale deep network”, Advances in neural information processing systems, Vol. 27

E. L. Denton, W. Zaremba, J. Bruna, Y. LeCun, and R. Fergus. (2014), “Exploiting linear structure within convolutional networks for efficient evaluation”, Advances in neural information processing systems, Vol. 27

S. Sukhbaatar, J. Bruna, M. Paluri, L. Bourdev, and R. Fergus. (2014), “Training convolutional networks with noisy labels”, IEEE Access, pages 220482-220495

D. Tran, L. D. Bourdev, R. Fergus, L. Torresani, and M. Paluri. (2014), “C3D: generic features for video analysis”, CoRR, abs/1412.0767, Vol. 2, pages 8

S. Sukhbaatar, and R. Fergus. (2014), “Learning from noisy labels with deep neural networks”, IEEE Trans. Neural Networks Learn. Syst., pages 8135-8153

M. Zeiler, and R. Fergus. (2014), “European conference on computer vision”, European Conference on Computer Vision., Berlin/Heidelberg, Germany: Springer, pages 818-833

M. Zeiler. (2014), “Computer vision–ECCV 2014”, (No Title), pages 818

N. Silberman, D. Sontag, and R. Fergus. (2014), “Instance segmentation of indoor scenes using a coverage loss”, Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part I 13, pages 616-631

E. L. Denton, W. Zaremba, J. Bruna, Y. LeCun, R. Fergus, Z. Ghahramani, M. Welling, C. Cortes, N. Lawrence, and K. Weinberger. (2014), “Advances in neural information processing systems”, Red Hook: Curran Associates, Inc

W. Zaremba, K. Kurach, and R. Fergus. (2014), “Learning to discover efficient mathematical identities”, Advances in Neural Information Processing Systems, Vol. 27

L. Bourdev, F. Yang, and R. Fergus. (2014), “Deep poselets for human detection”, arXiv preprint arXiv:1407.0717

R. Fergus, D. W. Hogg, R. Oppenheimer, D. Brenner, and L. Pueyo. (2014), “S4: A Spatial-spectral model for Speckle Suppression”, The Astrophysical Journal, Vol. 794, pages 161

S. Sukhbaatar, and R. Fergus. (2014), “Learning from noisy labels with deep neural networks. arXiv”, arXiv preprint arXiv:1406.2080

P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun. (2014), “OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks”, International Conference on Learning Representations (ICLR 2014)

J. Bruna, W. Zaremba, A. Szlam, and Y. LeCun. (2014), “Spectral Networks and Locally Connected Networks on Graphs”, International Conference on Learning Representations (ICLR 2014)

J. J. Tompson, A. Jain, Y. LeCun, and C. Bregler. (2014), “Joint training of a convolutional network and a graphical model for human pose estimation”, Advances in neural information processing systems, pages 1799-1807

J. Tompson, R. Goroshin, A. Jain, Y. LeCun, and C. Bregler. (2014), “Efficient object localization using convolutional networks”, computer Vision and Pattern Recognition (CVPR 2015)

J. Tompson, M. Stein, Y. Lecun, and K. Perlin. (2014), “Real-time continuous pose recovery of human hands using convolutional networks”, ACM Transactions on Graphics (ToG), Vol. 33, pages 1-10

J. Žbontar, and Y. LeCun. (2014), “Computing the stereo matching cost with a convolutional neural network”, Computer Vision and Pattern Recognition (CVPR 2015)

M. Mathieu, M. Henaff, and Y. LeCun. (2014), “Fast Training of Convolutional Networks through FFTs”, International Conference on Learning Representations (ICLR 2014)

N. Vasilache, J. Johnson, M. Mathieu, S. Chintala, S. Piantino, and Y. LeCun. (2014), “Fast Convolutional Nets With fbfft: A GPU Performance Evaluation”, International Conference on Learning Representations (ICLR 2015)

A. Jain, J. Tompson, Y. LeCun, and C. Bregler. (2014), “Modeep: A deep learning framework using motion features for human pose estimation”, Asian Conference on Computer Vision (ACCV)

D. Eigen, J. Rolfe, R. Fergus, and Y. LeCun. (2014), “Understanding Deep Architectures using a Recursive Convolutional Network”, International Conference on Learning Representations (ICLR 2014)

J. Bruna, A. Szlam, and Y. LeCun. (2014), “Signal Recovery from Lp Pooling Representations”, International Conference on Machine Learning (ICML 2014)

L. Sagun, V. U. Guney, G. B. Arous, and Y. LeCun. (2014), “Explorations on high dimensional landscapes”, arXiv preprint arXiv:1412.6615

C. Couprie, C. Farabet, L. Najman, and Y. LeCun. (2014), “Convolutional nets and watershed cuts for real-time semantic labeling of rgbd videos”, The Journal of Machine Learning Research, Vol. 15, pages 3489-3511

M. Bojarski, A. Choromanska, K. Choromanski, and Y. LeCun. (2014), “Differentially-and non-differentially-private random decision trees”, arXiv preprint arXiv:1410.6973

M. Mathieu, and Y. LeCun. (2014), “Fast approximation of rotations and Hessians matrices”, arXiv preprint arXiv:1404.7195

C. Couprie, C. Farabet, L. Najman, and Y. LeCun. (2014), “Toward real-time indoor semantic segmentation using depth information”, Journal of Machine Learning Research

M. Mathieu, M. Henaff, and Y. LeCun. (2014), “Fast training of convolutional networks through FFTS: international conference on learning representations (ICLR2014), CBLS, april 2014”, 2nd International Conference on Learning Representations, ICLR 2014

A. Ziskind, O. Hénaff, Y. LeCun, and D. Pelli. (2014), “The bottleneck in human letter recognition: A computational model”, Vision Sciences Society, pages 16-21

2013

C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. (2013), “Intriguing properties of neural networks”, arXiv preprint arXiv:1312.6199

P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun. (2013), “Overfeat: Integrated recognition, localization and detection using convolutional networks”, arXiv preprint arXiv:1312.6229

L. Wan, M. Zeiler, S. Zhang, Y. L. Cun, and R. Fergus. (2013), “Regularization of neural networks using dropconnect”, International conference on machine learning, pages 1058-1066

M. D. Zeiler, and R. Fergus. (2013), “Stochastic pooling for regularization of deep convolutional neural networks”, arXiv preprint arXiv:1301.3557

D. Eigen, D. Krishnan, and R. Fergus. (2013), “Restoring an image taken through a window covered with dirt or rain”, Proceedings of the IEEE international conference on computer vision, pages 633-640

C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. (2013), “Intriguing properties of neural networks. arXiv 2013”, arXiv preprint arXiv:1312.6199, Vol. 103

P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun. (2013), “OverFeat: Integrated Recognition”, Localization and Detection using Convolutional Networks, Vol. 17

B. Oppenheimer, C. Baranec, C. Beichman, D. Brenner, R. Burruss, E. Cady, J. Crepp, R. Dekany, R. Fergus, D. Hale, L. Hillenbrand, S. Hinkley, D. W. Hogg, D. King, E. Ligon, T. Lockhart, R. Nilsson, I. Parry, L. Pueyo, E. Rice, J. Roberts, L. Roberts, M. Shao, A. Sivaramakrishnan, R. Soummer, T. Truong, G. Vasisht, A. Veicht, F. Vescelus, J. Wallace, C. Zhai, and N. Zimmerman. (2013), “Reconnaissance of the HR 8799 exosolar system. I. Near-infrared spectroscopy”, The Astrophysical Journal, Vol. 768, pages 24

D. Eigen, J. Rolfe, R. Fergus, and Y. LeCun. (2013), “Understanding deep architectures using a recursive convolutional network”, arXiv preprint arXiv:1312.1847

K. Grauman, and R. Fergus. (2013), “Learning binary hash codes for large-scale image search”, Machine learning for computer vision, pages 49-87

C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. (2013), “Intriguing properties of neural networks. arXiv preprint (2013)”, arXiv preprint arXiv:1312.6199

D. Krishnan, J. Bruna, and R. Fergus. (2013), “Blind deconvolution with non-local sparsity reweighting”, arXiv preprint arXiv:1311.4029

D. W. Hogg, R. Angus, T. Barclay, R. Dawson, R. Fergus, D. Foreman-Mackey, S. Harmeling, M. Hirsch, D. Lang, B. T. Montet, D. Schiminovich, and B. Schölkopf. (2013), “Maximizing Kepler science return per telemetered pixel: Detailed models of the focal plane in the two-wheel era”, arXiv preprint arXiv:1309.0653

B. T. Montet, R. Angus, T. Barclay, R. Dawson, R. Fergus, D. Foreman-Mackey, S. Harmeling, M. Hirsch, D. W. Hogg, D. Lang, D. Schiminovich, and B. Scholkopf. (2013), “Maximizing Kepler science return per telemetered pixel: Searching the habitable zones of the brightest stars”, arXiv preprint arXiv:1309.0654

L. Pueyo, B. Oppenheimer, C. Beichman, D. Brenner, R. Burruss, E. Cady, J. Crepp, R. Dekany, R. Fergus, L. Hillenbrand, S. Hinkley, D. Hogg, D. King, E. Lingon, T. Lockhart, R. Nilsson, E. Rice, L. Roberts, J. Roberts, M. Shao, A. Sivaramakrishnan, R. Soummer, G. Vasisht, F. Vescelus, J. Wallace, C. Zhai, and N. Zimmerman. (2013), “Near Infra-red Integral Field Spectroscopy of the HR8799 Planetary System”, American Astronomical Society Meeting Abstracts# 221, Vol. 221, pages 126.02

C. Farabet, C. Couprie, L. Najman, and Y. LeCun. (2013), “Learning Hierarchical Features for Scene Labeling”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 8, pages 1915-1929

L. Wan, M. Zeiler, S. Zhang, Y. LeCun, and R. Fergus. (2013), “Regularization of neural networks using dropconnect”, 30th International Conference on Machine Learning (ICML 2013), pages 1058-1066

T. Vatanen, T. Raiko, H. Valpola, and Y. LeCun. (2013), “Pushing stochastic gradient towards second-order methods–backpropagation learning with transformations in nonlinearities”, Neural Information Processing: 20th International Conference, ICONIP 2013, Daegu, Korea, November 3-7, 2013. Proceedings, Part I 20, pages 442-449

P. Sermanet, K. Kavukcuoglu, S. Chintala, and Y. LeCun. (2013), “Pedestrian detection with unsupervised multi-stage feature learning”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2013), pages 3626-3633

C. Couprie, C. Farabet, L. Najman, and Y. LeCun. (2013), “Indoor Semantic Segmentation using depth information”, International Conference on Learning Representations (ICLR2013)

P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun. (2013), “OverFeat: Integrated Recognition”, Localization and Detection using Convolutional Networks, Vol. 17

E. J. Humphrey, J. P. Bello, and Y. LeCun. (2013), “Feature learning and deep architectures: New directions for music informatics”, Journal of Intelligent Information Systems, Vol. 41, pages 461-481

Y. LeCun, and M. Ranzato. (2013), “Deep learning tutorial”, Tutorials in international conference on machine learning (ICML’13), pages 1-29

J. T. Rolfe, and Y. LeCun. (2013), “Discriminative Recurrent Sparse Auto-Encoders”, international conference on learning representations (ICLR2013)

R. Goroshin, and Y. LeCun. (2013), “Saturating Auto-Encoder”, International Conference on Learning Representations (ICLR2013)

Y. LeCun. (2013), “Mnist handwritten digit database, yann lecun, corinna cortes and chris burges”, URL http://yann. lecun. com/exdb/mnist

J. Bruna, A. Szlam, and Y. LeCun. (2013), “Learning stable group invariant representations with convolutional networks”, arXiv preprint arXiv:1301.3537

C. Couprie, C. Farabet, and Y. LeCun. (2013), “Causal graph-based video segmentation”, 20th IEEE International Conference on Image Processing (ICIP 2013), pages 4249 – 4253

T. Schaul, and Y. LeCun. (2013), “Adaptive learning rates and parallelization for stochastic, sparse, non-smooth gradients”, International Conference on Learning Representations (ICLR2013)

U. A. Muller, L. D. Jackel, Y. LeCun, and B. Flepp. (2013), “Real-time adaptive off-road vehicle navigation and terrain classification”, Unmanned Systems Technology XV, Vol. 8741, pages 71-89

H. Glotin, Y. LeCun, T. Artieres, S. Mallat, O. Tchernichovski, and X. Halkias. (2013), “Neural information processing scaled for bioacoustics, from neurons to big data”, Proceedings of Neural Information Processing Scaled for Bioacoustics: from Neurons to Big Data, Vol. 2013

H. Glotin, C. Clark, Y. LeCun, P. Dugan, X. Halkias, and J. Sueur. (2013), “Bioacoustic challenges in icml4b”, in Proc. of 1st workshop on Machine Learning for Bioacoustics. No. USA, ISSN, pages 979-10

J. T. Rolfe, and Y. LeCun. (2013), “Discriminative recurrent sparse auto-encoders: 1st International Conference on Learning Representations, ICLR 2013”, 1st International Conference on Learning Representations, ICLR 2013

Y. Lecun. (2013), “Marc’Aurelio Ranzato”, Deep Learning Tutorial, ICML, Atlanta

C. Couprie, C. Farabet, L. Najman, and Y. LeCun. (2013), “Indoor semantic segmentation using depth information: 1st international conference on learning representations, iclr 2013”, 1st International Conference on Learning Representations, ICLR 2013

Y. LeCun. (2013), “Deep Learning & Convolutional Networks In Vision (part 2)”, VRML Paris

M. Pickett, B. Kuipers, Y. LeCun, and C. Morrison. (2013), “AAAI Workshop-Technical Report: Preface”, AAAI Workshop-Technical Report, pages vii

2012

N. Silberman, D. Hoiem, P. Kohli, and R. Fergus. (2012), “Indoor segmentation and support inference from rgbd images”, Computer Vision–ECCV 2012: 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part V 12, pages 746-760

Y. Weiss, R. Fergus, and A. Torralba. (2012), “Multidimensional spectral hashing”, Computer Vision–ECCV 2012: 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part V 12, pages 340-353

D. Eigen, and R. Fergus. (2012), “Nonparametric image parsing using adaptive neighbor sets”, 2012 IEEE Conference on Computer Vision and Pattern Recognition, pages 2799-2806

P. Hoiem, and R. Fergus. (2012), “Indoor segmentation and support inference from rgbd images”, ECCV. URL: https://www. microsoft. com/en-us/research/wp-content/uploads/2016/11/shkf_ eccv2012. pdf

L. Wan, L. Zhu, and R. Fergus. (2012), “A hybrid neural network-latent topic model”, Artificial Intelligence and Statistics, pages 1287-1294

M. D. Zeiler, and R. Fergus. (2012), “Differentiable pooling for hierarchical feature learning”, arXiv preprint arXiv:1207.0151

R. Fergus, M. Ranzato, R. Salakhutdinov, G. Taylor, and K. Yu. (2012), “Deep learning methods for vision”, Proceedings of the CVPR

T. Raiko, H. Valpola, and Y. LeCun. (2012), “Deep learning made easier by linear transformations in perceptrons”, International Conference on AI and Statistics (AISTAT’12)

P. Sermanet, S. Chintala, and Y. LeCun. (2012), “Convolutional neural networks applied to house numbers digit classification”, Proceedings of the 21st international conference on pattern recognition (ICPR2012), pages 3288-3291

T. Schaul, S. Zhang, and Y. LeCun. (2012), “No more pesky learning rates”, ICML, pages 343-351

J. M. Alvarez, T. Gevers, Y. LeCun, and A. M. Lopez. (2012), “Road scene segmentation from a single image”, Computer Vision–ECCV 2012: 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part VII 12, pages 376-389

C. Farabet, C. Couprie, L. Najman, and Y. LeCun. (2012), “Scene Parsing with Multiscale Feature Learning, Purity Trees, and Optimal Covers”, ICML

E. J. Humphrey, J. P. Bello, and Y. LeCun. (2012), “Moving beyond feature design: Deep architectures and automatic feature learning in music informatics.”, ISMIR, pages 403-408

Y. LeCun. (2012), “Learning invariant feature hierarchies”, Computer Vision–ECCV 2012. Workshops and Demonstrations: Florence, Italy, October 7-13, 2012, Proceedings, Part I 12, pages 496-505

P. Pham, D. Jelaca, C. Farabet, B. Martini, Y. LeCun, and E. Culurciello. (2012), “NeuFlow: Dataflow vision processing system-on-a-chip”, 2012 IEEE 55th International Midwest Symposium on Circuits and Systems (MWSCAS), pages 1044-1047

C. Farabet, R. Paz, J. Pérez-Carrasco, C. Zamarreño-Ramos, A. Linares-Barranco, Y. LeCun, E. Culurciello, T. Serrano-Gotarredona, and B. Linares-Barranco. (2012), “Comparison between frame-constrained fix-pixel-value and frame-free spiking-dynamic-pixel convnets for visual processing”, Frontiers in neuroscience, Vol. 6, pages 32

J. M. Alvarez, Y. LeCun, T. Gevers, and A. M. Lopez. (2012), “Semantic road segmentation via multi-scale ensembles of learned features”, Computer Vision–ECCV 2012. Workshops and Demonstrations: Florence, Italy, October 7-13, 2012, Proceedings, Part II 12, pages 586-595

A. Szlam, K. Gregor, and Y. LeCun. (2012), “Fast approximations to structured sparse coding and applications to object classification”, Computer Vision–ECCV 2012: 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part V 12, pages 200-213

Y. L. Cun, L. Jackel, B. Boser, and J. Denker. (2012), “Handwritten Digit Recognition: Applications of Neural Net Chips and Automatic”, Neuro-computing: Algorithms, Architectures and Applications, Vol. 68, pages 303

2011

M. D. Zeiler, G. W. Taylor, and R. Fergus. (2011), “Adaptive deconvolutional networks for mid and high level feature learning”, 2011 international conference on computer vision, pages 2018-2025

D. Krishnan, T. Tay, and R. Fergus. (2011), “Blind deconvolution using a normalized sparsity measure”, CVPR 2011, pages 233-240

N. Silberman, and R. Fergus. (2011), “Indoor scene segmentation using a structured light sensor”, 2011 IEEE international conference on computer vision workshops (ICCV workshops), pages 601-608

G. W. Taylor, I. Spiro, C. Bregler, and R. Fergus. (2011), “Learning invariance through imitation”, CVPR 2011, pages 2729-2736

M. Zeiler, G. W. Taylor, L. Sigal, I. Matthews, and R. Fergus. (2011), “Facial expression transfer with input-output temporal restricted boltzmann machines”, Advances in neural information processing systems, Vol. 24

P. Sermanet, and Y. LeCun. (2011), “Traffic sign recognition with multi-scale convolutional networks”, The 2011 international joint conference on neural networks, pages 2809-2813

C. Farabet, B. Martini, B. Corda, P. Akselrod, E. Culurciello, and Y. LeCun. (2011), “NeuFlow: A Runtime Reconfigurable Dataflow Processor for Vision”, Embedded Computer Vision Workshop at ICCV

Y. Boureau, N. L. Roux, F. Bach, J. Ponce, and Y. LeCun. (2011), “Ask the locals: multi-way local pooling for image recognition”, International Conference on Computer Vision (ICCV’11)

M. Henaff, K. Jarrett, K. Kavukcuoglu, and Y. LeCun. (2011), “UNSUPERVISED LEARNING OF SPARSE FEATURES FOR SCALABLE AUDIO CLASSIFICATION”, International Symposium on Music Information Retrieval

C. Farabet, Y. LeCun, K. Kavukcuoglu, E. Culurciello, B. Martini, P. Akselrod, and S. Talay. (2011), “Large-scale FPGA-based convolutional networks”, Scaling up machine learning: parallel and distributed approaches, Vol. 13, pages 399-419

K. Gregor, A. Szlam, and Y. LeCun. (2011), “Structured sparse coding via lateral inhibition”, Advances in Neural Information Processing Systems, Vol. 24

A. Szlam, K. Gregor, and Y. Cun. (2011), “Structured sparse coding via lateral inhibition”, Advances in Neural Information Processing Systems, Vol. 24

P. Sermanet, K. Kavukcuoglu, and Y. LeCun. (2011), “Traffic signs and pedestrians vision with multi-scale convolutional networks”, Snowbird Machine Learning Workshop, Vol. 2, pages 8

K. Gregor, and Y. LeCun. (2011), “Learning representations by maximizing compression”, arXiv preprint arXiv:1108.1169

P. Akselrod, F. Zhao, I. Derekli, C. Farabet, B. Martini, Y. LeCun, and E. Culurciello. (2011), “Hardware accelerated visual attention algorithm”, 2011 45th Annual Conference on Information Sciences and Systems, pages 1-6

K. Gregor, and Y. LeCun. (2011), “Efficient learning of sparse invariant representations”, arXiv preprint arXiv:1105.5307

C. Farabet, Y. LeCun, and E. Culurciello. (2011), “NeuFlow: A Runtime Reconfigurable Dataflow Architecture for Vision (abstract)”, Learning Workshop at Snowbird

J. Bello, Y. LeCun, and R. Rowe. (2011), “technical Perspective concerto for Violin and Markov Model”, Communications of the ACM, Vol. 54, pages 86

2010

M. D. Zeiler, D. Krishnan, G. W. Taylor, and R. Fergus. (2010), “Deconvolutional networks”, 2010 IEEE Computer Society Conference on computer vision and pattern recognition, pages 2528-2535

G. W. Taylor, R. Fergus, Y. LeCun, and C. Bregler. (2010), “Convolutional learning of spatio-temporal features”, Computer Vision–ECCV 2010: 11th European Conference on Computer Vision, Heraklion, Crete, Greece, September 5-11, 2010, Proceedings, Part VI 11, pages 140-153

R. Fergus, H. Bernal, Y. Weiss, and A. Torralba. (2010), “Semantic label sharing for learning with many categories”, Computer Vision–ECCV 2010: 11th European Conference on Computer Vision, Heraklion, Crete, Greece, September 5-11, 2010, Proceedings, Part I 11, pages 762-775

R. Fergus, L. Fei-Fei, P. Perona, and A. Zisserman. (2010), “Learning object categories from internet image searches”, Proceedings of the IEEE, Vol. 98, pages 1453-1466

N. Silberman, K. Ahrlich, R. Fergus, and L. Subramanian. (2010), “Case for automated detection of diabetic retinopathy”, 2010 AAAI spring symposium series

G. W. Taylor, R. Fergus, G. Williams, I. Spiro, and C. Bregler. (2010), “Pose-sensitive embedding by nonlinear nca regression”, Advances in Neural Information Processing Systems, Vol. 23

M. Zeiler, and R. Fergus. (2010), “Learning image decompositions with hierarchical sparse coding”, Tech. Rep. TR2010-935

N. Snavely, I. Simon, M. Goesele, R. Szeliski, S. Seitz, B. Kaneva, J. Sivic, A. Torralba, S. Avidan, W. Freeman, Z. Stone, T. Zickler, T. Darrell, T. Mei, X. Hua, T. Berg, A. Sorokin, G. Wang, D. Forsyth, D. Hoiem, I. Endres, A. Farhadi, R. Fergus, L. Fei-Fei, P. Perona, A. Zisserman, B. Russell, and J. Yuen. (2010), “INTERNET VISION”, Proceedings of the IEEE, Vol. 98

Y. LeCun, K. Kavukcuoglu, and C. Farabet. (2010), “Convolutional networks and applications in vision”, Proceedings of 2010 IEEE international symposium on circuits and systems, pages 253-256

K. Gregor, and Y. Lecun. (2010), “Learning fast approximations of sparse coding”, Machine Learning (ICML), 2010, International Conference on, pages 1-8

Y. Boureau, J. Ponce, and Y. LeCun. (2010), “A theoretical analysis of feature pooling in visual recognition”, Machine Learning (ICML), 2010, International Conference on

Y. Boureau, F. Bach, Y. LeCun, and J. Ponce. (2010), “Learning mid-level features for recognition”, Computer Vision and Pattern Recognition 2010. CVPR 2010. IEEE Conference on, pages 2559-2566

Y. LeCun, C. Cortes, and C. J. Burges. (2010), “MNIST handwritten digit database. 2010”, URL http://yann. lecun. com/exdb/mnist, Vol. 7, pages 6

G. Taylor, R. Fergus, Y. LeCun, and C. Bregler. (2010), “Convolutional learning of spatio-temporal features”, Computer Vision ( ECCV), 2010, European Conference on, pages 140-153

K. Kavukcuoglu, P. Sermanet, Y. Boureau, K. Gregor, M. Mathieu, and Y. LeCun. (2010), “Learning convolutional feature hierarchies for visual recognition”, Advances in Neural Information Processing Systems 23, NIPS 2010, Vol. 23

C. Farabet, B. Martini, P. Akselrod, S. Talay, Y. LeCun, and E. Culurciello. (2010), “Hardware accelerated convolutional neural networks for synthetic vision systems”, Proceedings of 2010 IEEE International Symposium on Circuits and Systems, pages 257-260

K. Kavukcuoglu, M. Ranzato, and Y. LeCun. (2010), “Fast inference in sparse coding algorithms with applications to object recognition”, arXiv preprint arXiv:1010.3467

G. Krouk, P. Mirowski, Y. LeCun, D. E. Shasha, and G. M. Coruzzi. (2010), “Predictive network modeling of the high-resolution dynamic plant transcriptome in response to nitrate”, Genome biology, Vol. 11, pages 1-19

Y. Boureau, J. Ponce, and Y. LeCun. (2010), “A theoretical analysis of feature pooling in vision algorithms”, Proc. International Conference on Machine learning (ICML’10), Vol. 28, pages 3

K. Gregor, and Y. LeCun. (2010), “Emergence of complex-like cells in a temporal product network with local receptive fields”, arXiv preprint arXiv:1006.0448

D. P. Kingma, and Y. Cun. (2010), “Regularized estimation of image statistics by score matching”, Advances in neural information processing systems, Vol. 23

A. Szlam, K. Kavukcuoglu, and Y. LeCun. (2010), “Convolutional matching pursuit and dictionary training”, arXiv preprint arXiv:1010.0422

P. Mirowski, M. Ranzato, and Y. LeCun. (2010), “Dynamic auto-encoders for semantic indexing”, Proceedings of the NIPS 2010 Workshop on Deep Learning, Vol. 2

L. Yann, C. Corinna, and C. Burges. (2010), “MNIST handwritten digit database (2010)”, Access mode: http://yann. lecun. com/exdb/mnist

M. K. Grimes, D. Anguelov, and Y. LeCun. (2010), “Hybrid hessians for flexible optimization of pose graphs”, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 2997-3004

C. Farabet, B. Martini, P. Akselrod, B. Corda, S. Talay, Y. LeCun, and E. Culurciello. (2010), “Bio-inspired vision processor for ultra-fast object categorization”, Proc. High Performance Embedded Computing Workshop

M. Scoffier, U. Muller, Y. LeCun, P. Sermanet, B. Corda, and C. Farabet. (2010), “Fully Adaptive Visual Navigation for Autonomous Vehicles”, Proc. Army Science Conference

A. Szlam, K. Kavukcuoglu, and Y. LeCun. (2010), “Convolutional K-SVD (abstract)”, Learning Workshop at Snowbird, pages 2

Y. Boureau, F. Bach, Y. LeCun, and J. Ponce. (2010), “Analysis of Feature Learning and Feature Pooling for Image Recognition (abstract)”, The Learning Workshop

2009

D. Krishnan, and R. Fergus. (2009), “Fast image deconvolution using hyper-Laplacian priors”, Advances in neural information processing systems, Vol. 22

K. Kavukcuoglu, M. Ranzato, R. Fergus, and Y. LeCun. (2009), “Learning invariant features through topographic filter maps”, 2009 ieee conference on computer vision and pattern recognition, pages 1605-1612

R. Fergus, Y. Weiss, and A. Torralba. (2009), “Semi-supervised learning in gigantic image collections”, Advances in neural information processing systems, Vol. 22

D. Krishnan, and R. Fergus. (2009), “Fast image deconvolution using hyper-laplacian priors, supplementary material”, Neural Information Pro-cessing Systems Conference

D. Krishnan, and R. Fergus. (2009), “Analytic hyper-laplacian priors for fast image deconvolution”, NIPS, Vol. 3, pages 7

D. Krishnan, and R. Fergus. (2009), “Dark Flash Photography”, ACM Transactions of Graphics

K. Jarrett, K. Kavukcuoglu, M. Ranzato, and Y. LeCun. (2009), “What is the best multi-stage architecture for object recognition?”, Computer Vision, 2009. ICCV 2009. IEEE 12th International Conference on, pages 2146-2153

C. Farabet, C. Poulet, J. Y. Han, and Y. LeCun. (2009), “Cnp: An fpga-based processor for convolutional networks”, 2009 International Conference on Field Programmable Logic and Applications, pages 32-37

P. Mirowski, D. Madhavan, Y. LeCun, and R. Kuzniecky. (2009), “Classification of patterns of EEG synchronization for seizure prediction”, Clinical neurophysiology, Vol. 120, pages 1927-1940

K. Kavukcuoglu, M. Ranzato, R. Fergus, and Y. LeCun. (2009), “Learning invariant features through topographic filter maps”, Computer Vision and Pattern Recognition 2009. CVPR 2009. IEEE Conference on, pages 1605-1612

R. Hadsell, P. Sermanet, J. Ben, A. Erkan, M. Scoffier, K. Kavukcuoglu, U. Muller, and Y. LeCun. (2009), “Learning long‐range vision for autonomous off‐road driving”, Journal of Field Robotics, Vol. 26, pages 120-144

Y. LeCun, C. Cortes, and C. J. Burges. (2009), “The MNIST database of handwritten digits (2010)”, URL http://yann. lecun. com/exdb/mnist

C. Farabet, C. Poulet, and Y. LeCun. (2009), “An fpga-based stream processor for embedded real-time vision with convolutional networks”, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops, pages 878-885

P. Mirowski, and Y. LeCun. (2009), “Dynamic factor graphs for time series modeling”, Machine Learning and Knowledge Discovery in Databases (MLKDD), 2009, pages 128-143

P. Sermanet, R. Hadsell, M. Scoffier, M. Grimes, J. Ben, A. Erkan, C. Crudele, U. Miller, and Y. LeCun. (2009), “A multirange architecture for collision‐free off‐road robot navigation”, Journal of Field Robotics, Vol. 26, pages 52-87

P. Sermanet, K. Kavukcuoglu, and Y. LeCun. (2009), “Eblearn: Open-source energy-based learning in c++”, 2009 21st IEEE International Conference on Tools with Artificial Intelligence, pages 693-697

M. Grimes, and Y. LeCun. (2009), “Efficient off-road localization using visually corrected odometry”, Robotics and Automation (ICRA), 2009. IEEE International Conference on, pages 2649-2654

C. Poulet, and Y. Lecun. (2009), “An FPGA-Based Stream Processor for Embedded Real-Time Vision with Convolutional Networks”, IEEE 12th International Conference on Computer Vision Workshops

K. Jarrett, K. Ranzato, and Y. LeCun. (2009), “High-Accuracy Object Recognition with a New Convolutional Net Architecture and Learning Algorithm (abstract)”, The Learning Workshop