Ilya Sutskever

Publication Activity (10 Years)

Years Active: 2007-2024
Publications (10 Years): 74

Top Venues

CoRR

ICLR (Poster)

ICML

NIPS

Publications

Hunter Lightman, Vineet Kosaraju, Yuri Burda, Harrison Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, Karl Cobbe
Let's Verify Step by Step. ICLR (2024)
Leo Gao, Tom Dupré la Tour, Henk Tillman, Gabriel Goh, Rajan Troll, Alec Radford, Ilya Sutskever, Jan Leike, Jeffrey Wu
Scaling and evaluating sparse autoencoders. CoRR (2024)
Stanislas Polu, Jesse Michael Han, Kunhao Zheng, Mantas Baksys, Igor Babuschkin, Ilya Sutskever
Formal Mathematics Statement Curriculum Learning. ICLR (2023)
Hunter Lightman, Vineet Kosaraju, Yura Burda, Harrison Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, Karl Cobbe
Let's Verify Step by Step. CoRR (2023)
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
Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision. CoRR (2023)
Yang Song, Prafulla Dhariwal, Mark Chen, Ilya Sutskever
Consistency Models. CoRR (2023)
Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, Ilya Sutskever
Robust Speech Recognition via Large-Scale Weak Supervision. ICML (2023)
Yang Song, Prafulla Dhariwal, Mark Chen, Ilya Sutskever
Consistency Models. ICML (2023)
Alexander Quinn Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, Mark Chen
GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models. ICML (2022)
Stanislas Polu, Jesse Michael Han, Kunhao Zheng, Mantas Baksys, Igor Babuschkin, Ilya Sutskever
Formal Mathematics Statement Curriculum Learning. CoRR (2022)
Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, Ilya Sutskever
Robust Speech Recognition via Large-Scale Weak Supervision. CoRR (2022)
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harrison 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
Evaluating Large Language Models Trained on Code. CoRR (2021)
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
Learning Transferable Visual Models From Natural Language Supervision. CoRR (2021)
Jesse Michael Han, Igor Babuschkin, Harrison Edwards, Arvind Neelakantan, Tao Xu, Stanislas Polu, Alex Ray, Pranav Shyam, Aditya Ramesh, Alec Radford, Ilya Sutskever
Unsupervised Neural Machine Translation with Generative Language Models Only. CoRR (2021)
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, Ilya Sutskever
Zero-Shot Text-to-Image Generation. ICML (2021)
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
Learning Transferable Visual Models From Natural Language Supervision. ICML (2021)
Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, Mark Chen
GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models. CoRR (2021)
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, Ilya Sutskever
Zero-Shot Text-to-Image Generation. CoRR (2021)
Stanislas Polu, Ilya Sutskever
Generative Language Modeling for Automated Theorem Proving. CoRR (2020)
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
Language Models are Few-Shot Learners. NeurIPS (2020)
Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever
Generative Pretraining From Pixels. ICML (2020)
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
Language Models are Few-Shot Learners. CoRR (2020)
Heewoo Jun, Rewon Child, Mark Chen, John Schulman, Aditya Ramesh, Alec Radford, Ilya Sutskever
Distribution Augmentation for Generative Modeling. ICML (2020)
Preetum Nakkiran, Gal Kaplun, Yamini Bansal, Tristan Yang, Boaz Barak, Ilya Sutskever
Deep Double Descent: Where Bigger Models and More Data Hurt. ICLR (2020)
Prafulla Dhariwal, Heewoo Jun, Christine Payne, Jong Wook Kim, Alec Radford, Ilya Sutskever
Jukebox: A Generative Model for Music. CoRR (2020)
Rewon Child, Scott Gray, Alec Radford, Ilya Sutskever
Generating Long Sequences with Sparse Transformers. CoRR (2019)
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
Dota 2 with Large Scale Deep Reinforcement Learning. CoRR (2019)
Preetum Nakkiran, Gal Kaplun, Yamini Bansal, Tristan Yang, Boaz Barak, Ilya Sutskever
Deep Double Descent: Where Bigger Models and More Data Hurt. CoRR (2019)
Daniel Huang, Prafulla Dhariwal, Dawn Song, Ilya Sutskever
GamePad: A Learning Environment for Theorem Proving. ICLR (Poster) (2019)
Will Grathwohl, Ricky T. Q. Chen, Jesse Bettencourt, Ilya Sutskever, David Duvenaud
FFJORD: Free-Form Continuous Dynamics for Scalable Reversible Generative Models. ICLR (2019)
Trapit Bansal, Jakub Pachocki, Szymon Sidor, Ilya Sutskever, Igor Mordatch
Emergent Complexity via Multi-Agent Competition. ICLR (Poster) (2018)
Daniel Huang, Prafulla Dhariwal, Dawn Song, Ilya Sutskever
GamePad: A Learning Environment for Theorem Proving. CoRR (2018)
Bradly C. Stadie, Ge Yang, Rein Houthooft, Xi Chen, Yan Duan, Yuhuai Wu, Pieter Abbeel, Ilya Sutskever
Some Considerations on Learning to Explore via Meta-Reinforcement Learning. CoRR (2018)
Will Grathwohl, Ricky T. Q. Chen, Jesse Bettencourt, Ilya Sutskever, David Duvenaud
FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models. CoRR (2018)
Maruan Al-Shedivat, Trapit Bansal, Yura Burda, Ilya Sutskever, Igor Mordatch, Pieter Abbeel
Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments. ICLR (2018)
Bradly C. Stadie, Ge Yang, Rein Houthooft, Xi Chen, Yan Duan, Yuhuai Wu, Pieter Abbeel, Ilya Sutskever
The Importance of Sampling inMeta-Reinforcement Learning. NeurIPS (2018)
Yuping Luo, Chung-Cheng Chiu, Navdeep Jaitly, Ilya Sutskever
Learning online alignments with continuous rewards policy gradient. ICASSP (2017)
Xi Chen, Diederik P. Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, Pieter Abbeel
Variational Lossy Autoencoder. ICLR (Poster) (2017)
Yan Duan, Marcin Andrychowicz, Bradly C. Stadie, Jonathan Ho, Jonas Schneider, Ilya Sutskever, Pieter Abbeel, Wojciech Zaremba
One-Shot Imitation Learning. NIPS (2017)
Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton
ImageNet classification with deep convolutional neural networks. Commun. ACM 60 (6) (2017)
Trapit Bansal, Jakub Pachocki, Szymon Sidor, Ilya Sutskever, Igor Mordatch
Emergent Complexity via Multi-Agent Competition. CoRR (2017)
Alec Radford, Rafal Józefowicz, Ilya Sutskever
Learning to Generate Reviews and Discovering Sentiment. CoRR (2017)
Chung-Cheng Chiu, Dieterich Lawson, Yuping Luo, George Tucker, Kevin Swersky, Ilya Sutskever, Navdeep Jaitly
An online sequence-to-sequence model for noisy speech recognition. CoRR (2017)
Maruan Al-Shedivat, Trapit Bansal, Yuri Burda, Ilya Sutskever, Igor Mordatch, Pieter Abbeel
Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments. CoRR (2017)
Bradly C. Stadie, Pieter Abbeel, Ilya Sutskever
Third Person Imitation Learning. ICLR (Poster) (2017)
Yan Duan, Marcin Andrychowicz, Bradly C. Stadie, Jonathan Ho, Jonas Schneider, Ilya Sutskever, Pieter Abbeel, Wojciech Zaremba
One-Shot Imitation Learning. CoRR (2017)
Bradly C. Stadie, Pieter Abbeel, Ilya Sutskever
Third-Person Imitation Learning. CoRR (2017)
Tim Salimans, Jonathan Ho, Xi Chen, Ilya Sutskever
Evolution Strategies as a Scalable Alternative to Reinforcement Learning. CoRR (2017)
Shixiang Gu, Timothy P. Lillicrap, Ilya Sutskever, Sergey Levine
Continuous Deep Q-Learning with Model-based Acceleration. CoRR (2016)
Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, Pieter Abbeel
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets. NIPS (2016)
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
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. CoRR (2016)
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
Mastering the game of Go with deep neural networks and tree search. Nat. 529 (7587) (2016)
Navdeep Jaitly, Quoc V. Le, Oriol Vinyals, Ilya Sutskever, David Sussillo, Samy Bengio
An Online Sequence-to-Sequence Model Using Partial Conditioning. NIPS (2016)
Karol Kurach, Marcin Andrychowicz, Ilya Sutskever
Neural Random Access Machines. ERCIM News 2016 (107) (2016)
Lukasz Kaiser, Ilya Sutskever
Neural GPUs Learn Algorithms. ICLR (Poster) (2016)
Yuping Luo, Chung-Cheng Chiu, Navdeep Jaitly, Ilya Sutskever
Learning Online Alignments with Continuous Rewards Policy Gradient. CoRR (2016)
Minh-Thang Luong, Quoc V. Le, Ilya Sutskever, Oriol Vinyals, Lukasz Kaiser
Multi-task Sequence to Sequence Learning. ICLR (Poster) (2016)
Shixiang Gu, Sergey Levine, Ilya Sutskever, Andriy Mnih
MuProp: Unbiased Backpropagation for Stochastic Neural Networks. ICLR (Poster) (2016)
Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, Pieter Abbeel
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets. CoRR (2016)
Eric Price, Wojciech Zaremba, Ilya Sutskever
Extensions and Limitations of the Neural GPU. CoRR (2016)
Diederik P. Kingma, Tim Salimans, Rafal Józefowicz, Xi Chen, Ilya Sutskever, Max Welling
Improving Variational Autoencoders with Inverse Autoregressive Flow. NIPS (2016)
Yan Duan, John Schulman, Xi Chen, Peter L. Bartlett, Ilya Sutskever, Pieter Abbeel
RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning. CoRR (2016)
Xi Chen, Diederik P. Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, Pieter Abbeel
Variational Lossy Autoencoder. CoRR (2016)
Arvind Neelakantan, Quoc V. Le, Ilya Sutskever
Neural Programmer: Inducing Latent Programs with Gradient Descent. ICLR (Poster) (2016)
Karol Kurach, Marcin Andrychowicz, Ilya Sutskever
Neural Random-Access Machines. ICLR (Poster) (2016)
Shixiang Gu, Timothy P. Lillicrap, Ilya Sutskever, Sergey Levine
Continuous Deep Q-Learning with Model-based Acceleration. ICML (2016)
Rafal Józefowicz, Wojciech Zaremba, Ilya Sutskever
An Empirical Exploration of Recurrent Network Architectures. ICML (2015)
Oriol Vinyals, Lukasz Kaiser, Terry Koo, Slav Petrov, Ilya Sutskever, Geoffrey E. Hinton
Grammar as a Foreign Language. NIPS (2015)
Ilya Sutskever, Rafal Józefowicz, Karol Gregor, Danilo Jimenez Rezende, Timothy P. Lillicrap, Oriol Vinyals
Towards Principled Unsupervised Learning. CoRR (2015)
Arvind Neelakantan, Luke Vilnis, Quoc V. Le, Ilya Sutskever, Lukasz Kaiser, Karol Kurach, James Martens
Adding Gradient Noise Improves Learning for Very Deep Networks. CoRR (2015)
Wojciech Zaremba, Ilya Sutskever
Reinforcement Learning Neural Turing Machines. CoRR (2015)
Navdeep Jaitly, Quoc V. Le, Oriol Vinyals, Ilya Sutskever, Samy Bengio
An Online Sequence-to-Sequence Model Using Partial Conditioning. CoRR (2015)
Thang Luong, Ilya Sutskever, Quoc V. Le, Oriol Vinyals, Wojciech Zaremba
Addressing the Rare Word Problem in Neural Machine Translation. ACL (1) (2015)
Chris J. Maddison, Aja Huang, Ilya Sutskever, David Silver
Move Evaluation in Go Using Deep Convolutional Neural Networks. ICLR (Poster) (2015)
David Eigen, Marc'Aurelio Ranzato, Ilya Sutskever
Learning Factored Representations in a Deep Mixture of Experts. ICLR (Workshop Poster) (2014)
Ilya Sutskever, Oriol Vinyals, Quoc V. Le
Sequence to Sequence Learning with Neural Networks. NIPS (2014)
Thang Luong, Ilya Sutskever, Quoc V. Le, Oriol Vinyals, Wojciech Zaremba
Addressing the Rare Word Problem in Neural Machine Translation. CoRR (2014)
Ilya Sutskever, Oriol Vinyals, Quoc V. Le
Sequence to Sequence Learning with Neural Networks. CoRR (2014)
Oriol Vinyals, Lukasz Kaiser, Terry Koo, Slav Petrov, Ilya Sutskever, Geoffrey E. Hinton
Grammar as a Foreign Language. CoRR (2014)
Wojciech Zaremba, Ilya Sutskever
Learning to Execute. CoRR (2014)
Nitish Srivastava, Geoffrey E. Hinton, Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov
Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15 (1) (2014)
Wojciech Zaremba, Ilya Sutskever, Oriol Vinyals
Recurrent Neural Network Regularization. CoRR (2014)
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. Goodfellow, Rob Fergus
Intriguing properties of neural networks. ICLR (Poster) (2014)
Tomás Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, Jeffrey Dean
Distributed Representations of Words and Phrases and their Compositionality. CoRR (2013)
Tomás Mikolov, Quoc V. Le, Ilya Sutskever
Exploiting Similarities among Languages for Machine Translation. CoRR (2013)
Ilya Sutskever, James Martens, George E. Dahl, Geoffrey E. Hinton
On the importance of initialization and momentum in deep learning. ICML (3) (2013)
Tomás Mikolov, Ilya Sutskever, Kai Chen, Gregory S. Corrado, Jeffrey Dean
Distributed Representations of Words and Phrases and their Compositionality. NIPS (2013)
Daniel Tarlow, Kevin Swersky, Laurent Charlin, Ilya Sutskever, Richard S. Zemel
Stochastic k-Neighborhood Selection for Supervised and Unsupervised Learning. ICML (3) (2013)
Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton
ImageNet Classification with Deep Convolutional Neural Networks. NIPS (2012)
James Martens, Ilya Sutskever, Kevin Swersky
Estimating the Hessian by Back-propagating Curvature. ICML (2012)
Kevin Swersky, Daniel Tarlow, Ilya Sutskever, Ruslan Salakhutdinov, Richard S. Zemel, Ryan P. Adams
Cardinality Restricted Boltzmann Machines. NIPS (2012)
Geoffrey E. Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov
Improving neural networks by preventing co-adaptation of feature detectors CoRR (2012)
Ilya Sutskever, James Martens, Geoffrey E. Hinton
Generating Text with Recurrent Neural Networks. ICML (2011)
James Martens, Ilya Sutskever
Learning Recurrent Neural Networks with Hessian-Free Optimization. ICML (2011)
James Martens, Ilya Sutskever
Parallelizable Sampling of Markov Random Fields. AISTATS (2010)
Ilya Sutskever, Geoffrey E. Hinton
Temporal-Kernel Recurrent Neural Networks. Neural Networks 23 (2) (2010)
Ilya Sutskever, Tijmen Tieleman
On the Convergence Properties of Contrastive Divergence. AISTATS (2010)
Ilya Sutskever, Ruslan Salakhutdinov, Joshua B. Tenenbaum
Modelling Relational Data using Bayesian Clustered Tensor Factorization. NIPS (2009)
Ilya Sutskever
A simpler unified analysis of budget perceptrons. ICML (2009)
Ilya Sutskever, Geoffrey E. Hinton, Graham W. Taylor
The Recurrent Temporal Restricted Boltzmann Machine. NIPS (2008)

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Publication Activity (10 Years)

Top Topics

Top Venues

Ilya Sutskever