Ilya Sutskever

109 papers A* 25A 4Misc 1Journal 52Unranked 25

Year	Rank	Type	Title / Venue / Authors
2025	A*	`conf`	Scaling and evaluating sparse autoencoders. ICLR Leo Gao, Tom Dupré la Tour, Henk Tillman, Gabriel Goh, Rajan Troll, Alec Radford, Ilya Sutskever, Jan Leike, Jeffrey Wu
2024	A*	`conf`	Let's Verify Step by Step. ICLR Hunter Lightman, Vineet Kosaraju, Yuri Burda, Harrison Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, Karl Cobbe
2024	J	`jnl`	Scaling and evaluating sparse autoencoders. CoRR Leo Gao, Tom Dupré la Tour, Henk Tillman, Gabriel Goh, Rajan Troll, Alec Radford, Ilya Sutskever, Jan Leike, Jeffrey Wu
2024	A*	`conf`	Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision. ICML Collin Burns, Pavel Izmailov, Jan Hendrik Kirchner, Bowen Baker, Leo Gao, Leopold Aschenbrenner, Yining Chen, Adrien Ecoffet, Manas Joglekar, Jan Leike, Ilya Sutskever, Jeffrey Wu
2023	A*	`conf`	Consistency Models. ICML Yang Song, Prafulla Dhariwal, Mark Chen, Ilya Sutskever
2023	J	`jnl`	Consistency Models. CoRR Yang Song, Prafulla Dhariwal, Mark Chen, Ilya Sutskever
2023	A*	`conf`	Formal Mathematics Statement Curriculum Learning. ICLR Stanislas Polu, Jesse Michael Han, Kunhao Zheng, Mantas Baksys, Igor Babuschkin, Ilya Sutskever
2023	J	`jnl`	Let's Verify Step by Step. CoRR Hunter Lightman, Vineet Kosaraju, Yura Burda, Harri Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, Karl Cobbe
2023	A*	`conf`	Robust Speech Recognition via Large-Scale Weak Supervision. ICML Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, Ilya Sutskever
2023	J	`jnl`	Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision. CoRR Collin Burns, Pavel Izmailov, Jan Hendrik Kirchner, Bowen Baker, Leo Gao, Leopold Aschenbrenner, Yining Chen, Adrien Ecoffet, Manas Joglekar, Jan Leike, Ilya Sutskever, Jeff Wu
2022	J	`jnl`	Formal Mathematics Statement Curriculum Learning. CoRR Stanislas Polu, Jesse Michael Han, Kunhao Zheng, Mantas Baksys, Igor Babuschkin, Ilya Sutskever
2022	A*	`conf`	GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models. ICML Alexander Quinn Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, Mark Chen
2022	J	`jnl`	Robust Speech Recognition via Large-Scale Weak Supervision. CoRR Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, Ilya Sutskever
2021	J	`jnl`	Evaluating Large Language Models Trained on Code. CoRR Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Pondé de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott Gray, Nick Ryder, Mikhail Pavlov, Alethea Power, Lukasz Kaiser, Mohammad Bavarian, Clemens Winter, Philippe Tillet, Felipe Petroski Such, Dave Cummings, Matthias Plappert, Fotios Chantzis, Elizabeth Barnes, Ariel Herbert-Voss, William Hebgen Guss, Alex Nichol, Alex Paino, Nikolas Tezak, Jie Tang, Igor Babuschkin, Suchir Balaji, Shantanu Jain, William Saunders, Christopher Hesse, Andrew N. Carr, Jan Leike, Joshua Achiam, Vedant Misra, Evan Morikawa, Alec Radford, Matthew Knight, Miles Brundage, Mira Murati, Katie Mayer, Peter Welinder, Bob McGrew, Dario Amodei, Sam McCandlish, Ilya Sutskever, Wojciech Zaremba
2021	J	`jnl`	GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models. CoRR Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, Mark Chen
2021	A*	`conf`	Learning Transferable Visual Models From Natural Language Supervision. ICML Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever
2021	J	`jnl`	Learning Transferable Visual Models From Natural Language Supervision. CoRR Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever
2021	J	`jnl`	Unsupervised Neural Machine Translation with Generative Language Models Only. CoRR Jesse Michael Han, Igor Babuschkin, Harrison Edwards, Arvind Neelakantan, Tao Xu, Stanislas Polu, Alex Ray, Pranav Shyam, Aditya Ramesh, Alec Radford, Ilya Sutskever
2021	A*	`conf`	Zero-Shot Text-to-Image Generation. ICML Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, Ilya Sutskever
2021	J	`jnl`	Zero-Shot Text-to-Image Generation. CoRR Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, Ilya Sutskever
2020	A*	`conf`	Deep Double Descent: Where Bigger Models and More Data Hurt. ICLR Preetum Nakkiran, Gal Kaplun, Yamini Bansal, Tristan Yang, Boaz Barak, Ilya Sutskever
2020	A*	`conf`	Distribution Augmentation for Generative Modeling. ICML Heewoo Jun, Rewon Child, Mark Chen, John Schulman, Aditya Ramesh, Alec Radford, Ilya Sutskever
2020	J	`jnl`	Generative Language Modeling for Automated Theorem Proving. CoRR Stanislas Polu, Ilya Sutskever
2020	A*	`conf`	Generative Pretraining From Pixels. ICML Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever
2020	J	`jnl`	Jukebox: A Generative Model for Music. CoRR Prafulla Dhariwal, Heewoo Jun, Christine Payne, Jong Wook Kim, Alec Radford, Ilya Sutskever
2020	A*	`conf`	Language Models are Few-Shot Learners. NeurIPS Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, Dario Amodei
2020	J	`jnl`	Language Models are Few-Shot Learners. CoRR Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, Dario Amodei
2019	J	`jnl`	Deep Double Descent: Where Bigger Models and More Data Hurt. CoRR Preetum Nakkiran, Gal Kaplun, Yamini Bansal, Tristan Yang, Boaz Barak, Ilya Sutskever
2019	J	`jnl`	Dota 2 with Large Scale Deep Reinforcement Learning. CoRR Christopher Berner, Greg Brockman, Brooke Chan, Vicki Cheung, Przemyslaw Debiak, Christy Dennison, David Farhi, Quirin Fischer, Shariq Hashme, Christopher Hesse, Rafal Józefowicz, Scott Gray, Catherine Olsson, Jakub Pachocki, Michael Petrov, Henrique Pondé de Oliveira Pinto, Jonathan Raiman, Tim Salimans, Jeremy Schlatter, Jonas Schneider, Szymon Sidor, Ilya Sutskever, Jie Tang, Filip Wolski, Susan Zhang
2019	—	`conf`	FFJORD: Free-Form Continuous Dynamics for Scalable Reversible Generative Models. ICLR Will Grathwohl, Ricky T. Q. Chen, Jesse Bettencourt, Ilya Sutskever, David Duvenaud
2019	—	`conf`	GamePad: A Learning Environment for Theorem Proving. ICLR (Poster) Daniel Huang, Prafulla Dhariwal, Dawn Song, Ilya Sutskever
2019	J	`jnl`	Generating Long Sequences with Sparse Transformers. CoRR Rewon Child, Scott Gray, Alec Radford, Ilya Sutskever
2018	—	`conf`	Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments. ICLR Maruan Al-Shedivat, Trapit Bansal, Yura Burda, Ilya Sutskever, Igor Mordatch, Pieter Abbeel
2018	—	`conf`	Emergent Complexity via Multi-Agent Competition. ICLR (Poster) Trapit Bansal, Jakub Pachocki, Szymon Sidor, Ilya Sutskever, Igor Mordatch
2018	J	`jnl`	FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models. CoRR Will Grathwohl, Ricky T. Q. Chen, Jesse Bettencourt, Ilya Sutskever, David Duvenaud
2018	J	`jnl`	GamePad: A Learning Environment for Theorem Proving. CoRR Daniel Huang, Prafulla Dhariwal, Dawn Song, Ilya Sutskever
2018	J	`jnl`	Some Considerations on Learning to Explore via Meta-Reinforcement Learning. CoRR Bradly C. Stadie, Ge Yang, Rein Houthooft, Xi Chen, Yan Duan, Yuhuai Wu, Pieter Abbeel, Ilya Sutskever
2018	A*	`conf`	The Importance of Sampling inMeta-Reinforcement Learning. NeurIPS Bradly C. Stadie, Ge Yang, Rein Houthooft, Xi Chen, Yan Duan, Yuhuai Wu, Pieter Abbeel, Ilya Sutskever
2017	J	`jnl`	An online sequence-to-sequence model for noisy speech recognition. CoRR Chung-Cheng Chiu, Dieterich Lawson, Yuping Luo, George Tucker, Kevin Swersky, Ilya Sutskever, Navdeep Jaitly
2017	J	`jnl`	Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments. CoRR Maruan Al-Shedivat, Trapit Bansal, Yuri Burda, Ilya Sutskever, Igor Mordatch, Pieter Abbeel
2017	J	`jnl`	Emergent Complexity via Multi-Agent Competition. CoRR Trapit Bansal, Jakub Pachocki, Szymon Sidor, Ilya Sutskever, Igor Mordatch
2017	J	`jnl`	Evolution Strategies as a Scalable Alternative to Reinforcement Learning. CoRR Tim Salimans, Jonathan Ho, Xi Chen, Ilya Sutskever
2017	J	`jnl`	ImageNet classification with deep convolutional neural networks. Commun. ACM Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton
2017	Misc	`conf`	Learning online alignments with continuous rewards policy gradient. ICASSP Yuping Luo, Chung-Cheng Chiu, Navdeep Jaitly, Ilya Sutskever
2017	J	`jnl`	Learning to Generate Reviews and Discovering Sentiment. CoRR Alec Radford, Rafal Józefowicz, Ilya Sutskever
2017	—	`conf`	One-Shot Imitation Learning. NIPS Yan Duan, Marcin Andrychowicz, Bradly C. Stadie, Jonathan Ho, Jonas Schneider, Ilya Sutskever, Pieter Abbeel, Wojciech Zaremba
2017	J	`jnl`	One-Shot Imitation Learning. CoRR Yan Duan, Marcin Andrychowicz, Bradly C. Stadie, Jonathan Ho, Jonas Schneider, Ilya Sutskever, Pieter Abbeel, Wojciech Zaremba
2017	—	`conf`	Third Person Imitation Learning. ICLR (Poster) Bradly C. Stadie, Pieter Abbeel, Ilya Sutskever
2017	J	`jnl`	Third-Person Imitation Learning. CoRR Bradly C. Stadie, Pieter Abbeel, Ilya Sutskever
2017	—	`conf`	Variational Lossy Autoencoder. ICLR (Poster) Xi Chen, Diederik P. Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, Pieter Abbeel
2016	—	`conf`	An Online Sequence-to-Sequence Model Using Partial Conditioning. NIPS Navdeep Jaitly, Quoc V. Le, Oriol Vinyals, Ilya Sutskever, David Sussillo, Samy Bengio
2016	A*	`conf`	Continuous Deep Q-Learning with Model-based Acceleration. ICML Shixiang Gu, Timothy P. Lillicrap, Ilya Sutskever, Sergey Levine
2016	J	`jnl`	Continuous Deep Q-Learning with Model-based Acceleration. CoRR Shixiang Gu, Timothy P. Lillicrap, Ilya Sutskever, Sergey Levine
2016	J	`jnl`	Extensions and Limitations of the Neural GPU. CoRR Eric Price, Wojciech Zaremba, Ilya Sutskever
2016	—	`conf`	Improving Variational Autoencoders with Inverse Autoregressive Flow. NIPS Diederik P. Kingma, Tim Salimans, Rafal Józefowicz, Xi Chen, Ilya Sutskever, Max Welling
2016	—	`conf`	InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets. NIPS Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, Pieter Abbeel
2016	J	`jnl`	InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets. CoRR Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, Pieter Abbeel
2016	J	`jnl`	Learning Online Alignments with Continuous Rewards Policy Gradient. CoRR Yuping Luo, Chung-Cheng Chiu, Navdeep Jaitly, Ilya Sutskever
2016	J	`jnl`	Mastering the game of Go with deep neural networks and tree search. Nat. David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Vedavyas Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy P. Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, Demis Hassabis
2016	A*	`conf`	MuProp: Unbiased Backpropagation for Stochastic Neural Networks. ICLR (Poster) Shixiang Gu, Sergey Levine, Ilya Sutskever, Andriy Mnih
2016	A*	`conf`	Multi-task Sequence to Sequence Learning. ICLR (Poster) Minh-Thang Luong, Quoc V. Le, Ilya Sutskever, Oriol Vinyals, Lukasz Kaiser
2016	A*	`conf`	Neural GPUs Learn Algorithms. ICLR (Poster) Lukasz Kaiser, Ilya Sutskever
2016	A*	`conf`	Neural Programmer: Inducing Latent Programs with Gradient Descent. ICLR (Poster) Arvind Neelakantan, Quoc V. Le, Ilya Sutskever
2016	J	`jnl`	Neural Random Access Machines. ERCIM News Karol Kurach, Marcin Andrychowicz, Ilya Sutskever
2016	A*	`conf`	Neural Random-Access Machines. ICLR (Poster) Karol Kurach, Marcin Andrychowicz, Ilya Sutskever
2016	J	`jnl`	RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning. CoRR Yan Duan, John Schulman, Xi Chen, Peter L. Bartlett, Ilya Sutskever, Pieter Abbeel
2016	J	`jnl`	TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. CoRR Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Gregory S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian J. Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Józefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mané, Rajat Monga, Sherry Moore, Derek Gordon Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul A. Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda B. Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, Xiaoqiang Zheng
2016	J	`jnl`	Variational Lossy Autoencoder. CoRR Xi Chen, Diederik P. Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, Pieter Abbeel
2015	J	`jnl`	Adding Gradient Noise Improves Learning for Very Deep Networks. CoRR Arvind Neelakantan, Luke Vilnis, Quoc V. Le, Ilya Sutskever, Lukasz Kaiser, Karol Kurach, James Martens
2015	—	`conf`	Addressing the Rare Word Problem in Neural Machine Translation. ACL (1) Thang Luong, Ilya Sutskever, Quoc V. Le, Oriol Vinyals, Wojciech Zaremba
2015	A*	`conf`	An Empirical Exploration of Recurrent Network Architectures. ICML Rafal Józefowicz, Wojciech Zaremba, Ilya Sutskever
2015	J	`jnl`	An Online Sequence-to-Sequence Model Using Partial Conditioning. CoRR Navdeep Jaitly, Quoc V. Le, Oriol Vinyals, Ilya Sutskever, Samy Bengio
2015	—	`conf`	Grammar as a Foreign Language. NIPS Oriol Vinyals, Lukasz Kaiser, Terry Koo, Slav Petrov, Ilya Sutskever, Geoffrey E. Hinton
2015	—	`conf`	Move Evaluation in Go Using Deep Convolutional Neural Networks. ICLR (Poster) Chris J. Maddison, Aja Huang, Ilya Sutskever, David Silver
2015	J	`jnl`	Reinforcement Learning Neural Turing Machines. CoRR Wojciech Zaremba, Ilya Sutskever
2015	J	`jnl`	Towards Principled Unsupervised Learning. CoRR Ilya Sutskever, Rafal Józefowicz, Karol Gregor, Danilo Jimenez Rezende, Timothy P. Lillicrap, Oriol Vinyals
2014	J	`jnl`	Addressing the Rare Word Problem in Neural Machine Translation. CoRR Thang Luong, Ilya Sutskever, Quoc V. Le, Oriol Vinyals, Wojciech Zaremba
2014	J	`jnl`	Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. Nitish Srivastava, Geoffrey E. Hinton, Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov
2014	J	`jnl`	Grammar as a Foreign Language. CoRR Oriol Vinyals, Lukasz Kaiser, Terry Koo, Slav Petrov, Ilya Sutskever, Geoffrey E. Hinton
2014	—	`conf`	Intriguing properties of neural networks. ICLR (Poster) Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. Goodfellow, Rob Fergus
2014	—	`conf`	Learning Factored Representations in a Deep Mixture of Experts. ICLR (Workshop Poster) David Eigen, Marc'Aurelio Ranzato, Ilya Sutskever
2014	J	`jnl`	Learning to Execute. CoRR Wojciech Zaremba, Ilya Sutskever
2014	J	`jnl`	Recurrent Neural Network Regularization. CoRR Wojciech Zaremba, Ilya Sutskever, Oriol Vinyals
2014	—	`conf`	Sequence to Sequence Learning with Neural Networks. NIPS Ilya Sutskever, Oriol Vinyals, Quoc V. Le
2014	J	`jnl`	Sequence to Sequence Learning with Neural Networks. CoRR Ilya Sutskever, Oriol Vinyals, Quoc V. Le
2013	—	`conf`	Distributed Representations of Words and Phrases and their Compositionality. NIPS Tomás Mikolov, Ilya Sutskever, Kai Chen, Gregory S. Corrado, Jeffrey Dean
2013	J	`jnl`	Distributed Representations of Words and Phrases and their Compositionality. CoRR Tomás Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, Jeffrey Dean
2013	J	`jnl`	Exploiting Similarities among Languages for Machine Translation. CoRR Tomás Mikolov, Quoc V. Le, Ilya Sutskever
2013	—	`conf`	On the importance of initialization and momentum in deep learning. ICML (3) Ilya Sutskever, James Martens, George E. Dahl, Geoffrey E. Hinton
2013	—	`conf`	Stochastic k-Neighborhood Selection for Supervised and Unsupervised Learning. ICML (3) Daniel Tarlow, Kevin Swersky, Laurent Charlin, Ilya Sutskever, Richard S. Zemel
2013	—	`—`	Training Recurrent Neural Networks. Ilya Sutskever
2012	—	`conf`	Cardinality Restricted Boltzmann Machines. NIPS Kevin Swersky, Daniel Tarlow, Ilya Sutskever, Ruslan Salakhutdinov, Richard S. Zemel, Ryan P. Adams
2012	A*	`conf`	Estimating the Hessian by Back-propagating Curvature. ICML James Martens, Ilya Sutskever, Kevin Swersky
2012	—	`conf`	ImageNet Classification with Deep Convolutional Neural Networks. NIPS Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton
2012	J	`jnl`	Improving neural networks by preventing co-adaptation of feature detectors CoRR Geoffrey E. Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov
2012	—	`ch.`	Training Deep and Recurrent Networks with Hessian-Free Optimization. Neural Networks: Tricks of the Trade (2nd ed.) James Martens, Ilya Sutskever
2011	A*	`conf`	Generating Text with Recurrent Neural Networks. ICML Ilya Sutskever, James Martens, Geoffrey E. Hinton
2011	A*	`conf`	Learning Recurrent Neural Networks with Hessian-Free Optimization. ICML James Martens, Ilya Sutskever
2010	A	`conf`	On the Convergence Properties of Contrastive Divergence. AISTATS Ilya Sutskever, Tijmen Tieleman
2010	A	`conf`	Parallelizable Sampling of Markov Random Fields. AISTATS James Martens, Ilya Sutskever
2010	J	`jnl`	Temporal-Kernel Recurrent Neural Networks. Neural Networks Ilya Sutskever, Geoffrey E. Hinton
2009	A*	`conf`	A simpler unified analysis of budget perceptrons. ICML Ilya Sutskever
2009	—	`conf`	Modelling Relational Data using Bayesian Clustered Tensor Factorization. NIPS Ilya Sutskever, Ruslan Salakhutdinov, Joshua B. Tenenbaum
2008	J	`jnl`	Deep, Narrow Sigmoid Belief Networks Are Universal Approximators. Neural Comput. Ilya Sutskever, Geoffrey E. Hinton
2008	—	`conf`	Mimicking Go Experts with Convolutional Neural Networks. ICANN (2) Ilya Sutskever, Vinod Nair
2008	—	`conf`	The Recurrent Temporal Restricted Boltzmann Machine. NIPS Ilya Sutskever, Geoffrey E. Hinton, Graham W. Taylor
2008	—	`conf`	Using matrices to model symbolic relationship. NIPS Ilya Sutskever, Geoffrey E. Hinton
2007	A	`conf`	Learning Multilevel Distributed Representations for High-Dimensional Sequences. AISTATS Ilya Sutskever, Geoffrey E. Hinton
2007	A	`conf`	Visualizing Similarity Data with a Mixture of Maps. AISTATS James Cook, Ilya Sutskever, Andriy Mnih, Geoffrey E. Hinton