Visualizing Similarity Data with a Mixture of Maps. - James Cook; Ilya Sutskever; Andriy Mnih; Geoffrey E. Hinton (AISTATS, 2007)

Learning Multilevel Distributed Representations for High-Dimensional Sequences. - Ilya Sutskever; Geoffrey E. Hinton (AISTATS, 2007)

Mimicking Go Experts with Convolutional Neural Networks. - Ilya Sutskever; Vinod Nair (ICANN (2), 2008)

Using matrices to model symbolic relationship. - Ilya Sutskever; Geoffrey E. Hinton (NIPS, 2008)

The Recurrent Temporal Restricted Boltzmann Machine. - Ilya Sutskever; Geoffrey E. Hinton; Graham W. Taylor (NIPS, 2008)

Deep, Narrow Sigmoid Belief Networks Are Universal Approximators. - Ilya Sutskever; Geoffrey E. Hinton (Neural Comput., 2008)

A simpler unified analysis of budget perceptrons. - Ilya Sutskever (ICML, 2009)

Modelling Relational Data using Bayesian Clustered Tensor Factorization. - Ilya Sutskever; Ruslan Salakhutdinov; Joshua B. Tenenbaum (NIPS, 2009)

Parallelizable Sampling of Markov Random Fields. - James Martens; Ilya Sutskever (AISTATS, 2010)

On the Convergence Properties of Contrastive Divergence. - Ilya Sutskever; Tijmen Tieleman (AISTATS, 2010)

Temporal-Kernel Recurrent Neural Networks. - Ilya Sutskever; Geoffrey E. Hinton (Neural Networks, 2010)

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

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

Estimating the Hessian by Back-propagating Curvature. - James Martens; Ilya Sutskever; Kevin Swersky (ICML, 2012)

Cardinality Restricted Boltzmann Machines. - Kevin Swersky; Daniel Tarlow; Ilya Sutskever; Ruslan Salakhutdinov; Richard S. Zemel; Ryan P. Adams (NIPS, 2012)

Improving neural networks by preventing co-adaptation of feature detectors - Geoffrey E. Hinton; Nitish Srivastava; Alex Krizhevsky; Ilya Sutskever; Ruslan Salakhutdinov (CoRR, 2012)

Training Deep and Recurrent Networks with Hessian-Free Optimization. - James Martens; Ilya Sutskever (Neural Networks: Tricks of the Trade (2nd ed.), 2012)

On the importance of initialization and momentum in deep learning. - Ilya Sutskever; James Martens; George E. Dahl; Geoffrey E. Hinton (ICML (3), 2013)

Stochastic k-Neighborhood Selection for Supervised and Unsupervised Learning. - Daniel Tarlow; Kevin Swersky; Laurent Charlin; Ilya Sutskever; Richard S. Zemel (ICML (3), 2013)

Distributed Representations of Words and Phrases and their Compositionality. - Tomás Mikolov; Ilya Sutskever; Kai Chen 010; Gregory S. Corrado; Jeffrey Dean (NIPS, 2013)

Exploiting Similarities among Languages for Machine Translation. - Tomás Mikolov; Quoc V. Le; Ilya Sutskever (CoRR, 2013)

Training Recurrent Neural Networks. - Ilya Sutskever (2013)

Sequence to Sequence Learning with Neural Networks. - Ilya Sutskever; Oriol Vinyals; Quoc V. Le (NIPS, 2014)

Learning Factored Representations in a Deep Mixture of Experts. - David Eigen; Marc'Aurelio Ranzato; Ilya Sutskever (ICLR (Workshop Poster), 2014)

Intriguing properties of neural networks. - Christian Szegedy; Wojciech Zaremba; Ilya Sutskever; Joan Bruna; Dumitru Erhan; Ian J. Goodfellow; Rob Fergus (ICLR (Poster), 2014)

Learning to Execute. - Wojciech Zaremba; Ilya Sutskever (CoRR, 2014)

Recurrent Neural Network Regularization. - Wojciech Zaremba; Ilya Sutskever; Oriol Vinyals (CoRR, 2014)

Dropout: a simple way to prevent neural networks from overfitting. - Nitish Srivastava; Geoffrey E. Hinton; Alex Krizhevsky; Ilya Sutskever; Ruslan Salakhutdinov (J. Mach. Learn. Res., 2014)

Addressing the Rare Word Problem in Neural Machine Translation. - Thang Luong; Ilya Sutskever; Quoc V. Le; Oriol Vinyals; Wojciech Zaremba (ACL (1), 2015)

An Empirical Exploration of Recurrent Network Architectures. - Rafal Józefowicz; Wojciech Zaremba; Ilya Sutskever (ICML, 2015)

Grammar as a Foreign Language. - Oriol Vinyals; Lukasz Kaiser; Terry Koo; Slav Petrov; Ilya Sutskever; Geoffrey E. Hinton (NIPS, 2015)

An Online Sequence-to-Sequence Model Using Partial Conditioning. - Navdeep Jaitly; Quoc V. Le; Oriol Vinyals; Ilya Sutskever; Samy Bengio (CoRR, 2015)

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

Adding Gradient Noise Improves Learning for Very Deep Networks. - Arvind Neelakantan; Luke Vilnis; Quoc V. Le; Ilya Sutskever; Lukasz Kaiser; Karol Kurach; James Martens (CoRR, 2015)

Towards Principled Unsupervised Learning. - Ilya Sutskever; Rafal Józefowicz; Karol Gregor; Danilo Jimenez Rezende; Timothy P. Lillicrap; Oriol Vinyals (CoRR, 2015)

Reinforcement Learning Neural Turing Machines. - Wojciech Zaremba; Ilya Sutskever (CoRR, 2015)

Continuous Deep Q-Learning with Model-based Acceleration. - Shixiang Gu; Timothy P. Lillicrap; Ilya Sutskever; Sergey Levine (ICML, 2016)

InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets. - Xi Chen 022; Yan Duan; Rein Houthooft; John Schulman; Ilya Sutskever; Pieter Abbeel (NIPS, 2016)

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

Improving Variational Autoencoders with Inverse Autoregressive Flow. - Diederik P. Kingma; Tim Salimans; Rafal Józefowicz; Xi Chen 022; Ilya Sutskever; Max Welling (NIPS, 2016)

TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. - Martín Abadi; Ashish Agarwal; Paul Barham 001; Eugene Brevdo; Zhifeng Chen; Craig Citro; Gregory S. Corrado; Andy Davis; Jeffrey Dean; Matthieu Devin; Sanjay Ghemawat; Ian J. Goodfellow; et al. (CoRR, 2016)

RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning. - Yan Duan; John Schulman; Xi Chen 022; Peter L. Bartlett; Ilya Sutskever; Pieter Abbeel (CoRR, 2016)

MuProp: Unbiased Backpropagation for Stochastic Neural Networks. - Shixiang Gu; Sergey Levine; Ilya Sutskever; Andriy Mnih (ICLR (Poster), 2016)

Neural GPUs Learn Algorithms. - Lukasz Kaiser; Ilya Sutskever (ICLR (Poster), 2016)

Multi-task Sequence to Sequence Learning. - Minh-Thang Luong; Quoc V. Le; Ilya Sutskever; Oriol Vinyals; Lukasz Kaiser (ICLR (Poster), 2016)

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

Extensions and Limitations of the Neural GPU. - Eric Price 001; Wojciech Zaremba; Ilya Sutskever (CoRR, 2016)

Neural Random Access Machines. - Karol Kurach; Marcin Andrychowicz; Ilya Sutskever (ERCIM News, 2016)

Mastering the game of Go with deep neural networks and tree search. - David Silver; Aja Huang; Chris J. Maddison; Arthur Guez; Laurent Sifre; George van den Driessche 002; Julian Schrittwieser; Ioannis Antonoglou; Vedavyas Panneershelvam; Marc Lanctot; Sander Dieleman; Dominik Grewe; et al. (Nat., 2016)

Learning online alignments with continuous rewards policy gradient. - Yuping Luo; Chung-Cheng Chiu; Navdeep Jaitly; Ilya Sutskever (ICASSP, 2017)

Variational Lossy Autoencoder. - Xi Chen 022; Diederik P. Kingma; Tim Salimans; Yan Duan; Prafulla Dhariwal; John Schulman; Ilya Sutskever; Pieter Abbeel (ICLR (Poster), 2017)

Third Person Imitation Learning. - Bradly C. Stadie; Pieter Abbeel; Ilya Sutskever (ICLR (Poster), 2017)

One-Shot Imitation Learning. - Yan Duan; Marcin Andrychowicz; Bradly C. Stadie; Jonathan Ho; Jonas Schneider 002; Ilya Sutskever; Pieter Abbeel; Wojciech Zaremba (NIPS, 2017)

ImageNet classification with deep convolutional neural networks. - Alex Krizhevsky; Ilya Sutskever; Geoffrey E. Hinton (Commun. ACM, 2017)

An online sequence-to-sequence model for noisy speech recognition. - Chung-Cheng Chiu; Dieterich Lawson; Yuping Luo; George Tucker; Kevin Swersky; Ilya Sutskever; Navdeep Jaitly (CoRR, 2017)

Learning to Generate Reviews and Discovering Sentiment. - Alec Radford; Rafal Józefowicz; Ilya Sutskever (CoRR, 2017)

Evolution Strategies as a Scalable Alternative to Reinforcement Learning. - Tim Salimans; Jonathan Ho; Xi Chen 022; Ilya Sutskever (CoRR, 2017)

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 (ICLR (Poster), 2018)

The Importance of Sampling inMeta-Reinforcement Learning. - Bradly C. Stadie; Ge Yang 003; Rein Houthooft; Xi Chen 022; Yan Duan; Yuhuai Wu; Pieter Abbeel; Ilya Sutskever (NeurIPS, 2018)

Some Considerations on Learning to Explore via Meta-Reinforcement Learning. - Bradly C. Stadie; Ge Yang 003; Rein Houthooft; Xi Chen 022; Yan Duan; Yuhuai Wu; Pieter Abbeel; Ilya Sutskever (CoRR, 2018)

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 001; Prafulla Dhariwal; Dawn Song; Ilya Sutskever (ICLR (Poster), 2019)

Generating Long Sequences with Sparse Transformers. - Rewon Child; Scott Gray; Alec Radford; Ilya Sutskever (CoRR, 2019)

Dota 2 with Large Scale Deep Reinforcement Learning. - Christopher Berner; Greg Brockman; Brooke Chan; Vicki Cheung; Przemyslaw Debiak; Christy Dennison; David Farhi; Quirin Fischer; Shariq Hashme; Christopher Hesse; Rafal Józefowicz; Scott Gray; et al. (CoRR, 2019)

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

Generative Pretraining From Pixels. - Mark Chen 003; Alec Radford; Rewon Child; Jeffrey Wu 003; Heewoo Jun; David Luan; Ilya Sutskever (ICML, 2020)

Distribution Augmentation for Generative Modeling. - Heewoo Jun; Rewon Child; Mark Chen 003; John Schulman; Aditya Ramesh; Alec Radford; Ilya Sutskever (ICML, 2020)

Language Models are Few-Shot Learners. - 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; et al. (NeurIPS, 2020)

Jukebox: A Generative Model for Music. - Prafulla Dhariwal; Heewoo Jun; Christine Payne; Jong Wook Kim; Alec Radford; Ilya Sutskever (CoRR, 2020)

Generative Language Modeling for Automated Theorem Proving. - Stanislas Polu; Ilya Sutskever (CoRR, 2020)

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 003; Ilya Sutskever (ICML, 2021)

Evaluating Large Language Models Trained on Code. - Mark Chen 003; 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; et al. (CoRR, 2021)

Unsupervised Neural Machine Translation with Generative Language Models Only. - Jesse Michael Han; Igor Babuschkin; Harrison Edwards; Arvind Neelakantan; Tao Xu; Stanislas Polu; Alex Ray; Pranav Shyam; Aditya Ramesh; Alec Radford; Ilya Sutskever (CoRR, 2021)

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 003 (ICML, 2022)

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

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

Consistency Models. - Yang Song; Prafulla Dhariwal; Mark Chen 003; Ilya Sutskever (ICML, 2023)

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 (ICLR, 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 003 (ICML, 2024)

Scaling and evaluating sparse autoencoders. - Leo Gao; Tom Dupré la Tour; Henk Tillman; Gabriel Goh; Rajan Troll; Alec Radford; Ilya Sutskever; Jan Leike; Jeffrey Wu 003 (ICLR, 2025)