Sutskever, Ilya

53 publications

ICLR 2025 Scaling and Evaluating Sparse Autoencoders Leo Gao, Tom Dupre la Tour, Henk Tillman, Gabriel Goh, Rajan Troll, Alec Radford, Ilya Sutskever, Jan Leike, Jeffrey Wu

ICLR 2024 Let's Verify Step by Step Hunter Lightman, Vineet Kosaraju, Yuri Burda, Harrison Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, Karl Cobbe

ICML 2024 Weak-to-Strong Generalization: Eliciting Strong Capabilities with Weak Supervision 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

ICML 2023 Consistency Models Yang Song, Prafulla Dhariwal, Mark Chen, Ilya Sutskever

ICLR 2023 Formal Mathematics Statement Curriculum Learning Stanislas Polu, Jesse Michael Han, Kunhao Zheng, Mantas Baksys, Igor Babuschkin, Ilya Sutskever

ICML 2023 Robust Speech Recognition via Large-Scale Weak Supervision Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine Mcleavey, Ilya Sutskever

ICML 2022 GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models Alexander Quinn Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob Mcgrew, Ilya Sutskever, Mark Chen

ICML 2021 Learning Transferable Visual Models from Natural Language Supervision 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

ICML 2021 Zero-Shot Text-to-Image Generation Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, Ilya Sutskever

ICLR 2020 Deep Double Descent: Where Bigger Models and More Data Hurt Preetum Nakkiran, Gal Kaplun, Yamini Bansal, Tristan Yang, Boaz Barak, Ilya Sutskever

ICML 2020 Distribution Augmentation for Generative Modeling Heewoo Jun, Rewon Child, Mark Chen, John Schulman, Aditya Ramesh, Alec Radford, Ilya Sutskever

ICML 2020 Generative Pretraining from Pixels Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever

NeurIPS 2020 Language Models Are Few-Shot Learners Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D 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 Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, Dario Amodei

ICLR 2019 FFJORD: Free-Form Continuous Dynamics for Scalable Reversible Generative Models Will Grathwohl, Ricky T. Q. Chen, Jesse Bettencourt, Ilya Sutskever, David Duvenaud

ICLR 2019 GamePad: A Learning Environment for Theorem Proving Daniel Huang, Prafulla Dhariwal, Dawn Song, Ilya Sutskever

ICLR 2018 Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments Maruan Al-Shedivat, Trapit Bansal, Yura Burda, Ilya Sutskever, Igor Mordatch, Pieter Abbeel

ICLR 2018 Emergent Complexity via Multi-Agent Competition Trapit Bansal, Jakub Pachocki, Szymon Sidor, Ilya Sutskever, Igor Mordatch

NeurIPS 2018 The Importance of Sampling inMeta-Reinforcement Learning Bradly Stadie, Ge Yang, Rein Houthooft, Peter Chen, Yan Duan, Yuhuai Wu, Pieter Abbeel, Ilya Sutskever

NeurIPS 2017 One-Shot Imitation Learning Yan Duan, Marcin Andrychowicz, Bradly Stadie, OpenAI Jonathan Ho, Jonas Schneider, Ilya Sutskever, Pieter Abbeel, Wojciech Zaremba

ICLR 2017 Third Person Imitation Learning Bradly C. Stadie, Pieter Abbeel, Ilya Sutskever

ICLR 2017 Variational Lossy Autoencoder Xi Chen, Diederik P. Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, Pieter Abbeel

NeurIPS 2016 An Online Sequence-to-Sequence Model Using Partial Conditioning Navdeep Jaitly, Quoc V Le, Oriol Vinyals, Ilya Sutskever, David Sussillo, Samy Bengio

ICML 2016 Continuous Deep Q-Learning with Model-Based Acceleration Shixiang Gu, Timothy Lillicrap, Ilya Sutskever, Sergey Levine

NeurIPS 2016 Improved Variational Inference with Inverse Autoregressive Flow Diederik P. Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, Max Welling

NeurIPS 2016 InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, Pieter Abbeel

ICLR 2016 MuProp: Unbiased Backpropagation for Stochastic Neural Networks Shixiang Gu, Sergey Levine, Ilya Sutskever, Andriy Mnih

ICLR 2016 Multi-Task Sequence to Sequence Learning Minh-Thang Luong, Quoc V. Le, Ilya Sutskever, Oriol Vinyals, Lukasz Kaiser

ICLR 2016 Neural GPUs Learn Algorithms Lukasz Kaiser, Ilya Sutskever

ICLR 2016 Neural Programmer: Inducing Latent Programs with Gradient Descent Arvind Neelakantan, Quoc V. Le, Ilya Sutskever

ICLR 2016 Neural Random-Access Machines Karol Kurach, Marcin Andrychowicz, Ilya Sutskever

ICML 2015 An Empirical Exploration of Recurrent Network Architectures Rafal Jozefowicz, Wojciech Zaremba, Ilya Sutskever

NeurIPS 2015 Grammar as a Foreign Language Oriol Vinyals, Łukasz Kaiser, Terry Koo, Slav Petrov, Ilya Sutskever, Geoffrey Hinton

ICLR 2015 Move Evaluation in Go Using Deep Convolutional Neural Networks Chris J. Maddison, Aja Huang, Ilya Sutskever, David Silver

JMLR 2014 Dropout: A Simple Way to Prevent Neural Networks from Overfitting Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov

ICLR 2014 Intriguing Properties of Neural Networks Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. Goodfellow, Rob Fergus

ICLR 2014 Learning Factored Representations in a Deep Mixture of Experts David Eigen, Marc'Aurelio Ranzato, Ilya Sutskever

NeurIPS 2014 Sequence to Sequence Learning with Neural Networks Ilya Sutskever, Oriol Vinyals, Quoc V Le

NeurIPS 2013 Distributed Representations of Words and Phrases and Their Compositionality Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, Jeff Dean

ICML 2013 On the Importance of Initialization and Momentum in Deep Learning Ilya Sutskever, James Martens, George Dahl, Geoffrey Hinton

ICML 2013 Stochastic K-Neighborhood Selection for Supervised and Unsupervised Learning Daniel Tarlow, Kevin Swersky, Laurent Charlin, Ilya Sutskever, Rich Zemel

NeurIPS 2012 Cardinality Restricted Boltzmann Machines Kevin Swersky, Ilya Sutskever, Daniel Tarlow, Richard S. Zemel, Ruslan Salakhutdinov, Ryan P. Adams

ICML 2012 Estimating the Hessian by Back-Propagating Curvature James Martens, Ilya Sutskever, Kevin Swersky

NeurIPS 2012 ImageNet Classification with Deep Convolutional Neural Networks Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton

ICML 2011 Generating Text with Recurrent Neural Networks Ilya Sutskever, James Martens, Geoffrey E. Hinton

ICML 2011 Learning Recurrent Neural Networks with Hessian-Free Optimization James Martens, Ilya Sutskever

AISTATS 2010 On the Convergence Properties of Contrastive Divergence Ilya Sutskever, Tijmen Tieleman

AISTATS 2010 Parallelizable Sampling of Markov Random Fields James Martens, Ilya Sutskever

ICML 2009 A Simpler Unified Analysis of Budget Perceptrons Ilya Sutskever

NeurIPS 2009 Modelling Relational Data Using Bayesian Clustered Tensor Factorization Ilya Sutskever, Joshua B. Tenenbaum, Ruslan Salakhutdinov

NeurIPS 2008 The Recurrent Temporal Restricted Boltzmann Machine Ilya Sutskever, Geoffrey E. Hinton, Graham W. Taylor

NeurIPS 2008 Using Matrices to Model Symbolic Relationship Ilya Sutskever, Geoffrey E. Hinton

AISTATS 2007 Learning Multilevel Distributed Representations for High-Dimensional Sequences Ilya Sutskever, Geoffrey Hinton

AISTATS 2007 Visualizing Similarity Data with a Mixture of Maps James Cook, Ilya Sutskever, Andriy Mnih, Geoffrey Hinton