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ORCIDIlya Sutskever
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- affiliation: OpenAI, San Francisco, CA, USA
- affiliation: Google Inc, Mountain View, CA, USA
- affiliation (PhD 2013): University of Toronto, Canada
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2020 – today
- 2024
[i47]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 abs/2406.04093 (2024)- 2023
- [c52]Stanislas Polu, Jesse Michael Han, Kunhao Zheng, Mantas Baksys, Igor Babuschkin, Ilya Sutskever:
Formal Mathematics Statement Curriculum Learning. ICLR 2023 - [c51]Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, Ilya Sutskever:
Robust Speech Recognition via Large-Scale Weak Supervision. ICML 2023: 28492-28518 - [c50]Yang Song, Prafulla Dhariwal, Mark Chen, Ilya Sutskever:
Consistency Models. ICML 2023: 32211-32252 - [i46]Yang Song, Prafulla Dhariwal, Mark Chen, Ilya Sutskever:
Consistency Models. CoRR abs/2303.01469 (2023) - [i45]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 abs/2305.20050 (2023) - [i44]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 abs/2312.09390 (2023) - 2022
- [c49]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: 16784-16804 - [i43]Stanislas Polu, Jesse Michael Han, Kunhao Zheng, Mantas Baksys, Igor Babuschkin, Ilya Sutskever:
Formal Mathematics Statement Curriculum Learning. CoRR abs/2202.01344 (2022) - [i42]Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, Ilya Sutskever:
Robust Speech Recognition via Large-Scale Weak Supervision. CoRR abs/2212.04356 (2022) - 2021
- [c48]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: 8748-8763 - [c47]Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, Ilya Sutskever:
Zero-Shot Text-to-Image Generation. ICML 2021: 8821-8831 - [i41]Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, Ilya Sutskever:
Zero-Shot Text-to-Image Generation. CoRR abs/2102.12092 (2021) - [i40]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 abs/2103.00020 (2021) - [i39]Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Pondé 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 abs/2107.03374 (2021) - [i38]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 abs/2110.05448 (2021) - [i37]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 abs/2112.10741 (2021) - 2020
- [c46]Preetum Nakkiran, Gal Kaplun, Yamini Bansal, Tristan Yang, Boaz Barak, Ilya Sutskever:
Deep Double Descent: Where Bigger Models and More Data Hurt. ICLR 2020 - [c45]Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever:
Generative Pretraining From Pixels. ICML 2020: 1691-1703 - [c44]Heewoo Jun, Rewon Child, Mark Chen, John Schulman, Aditya Ramesh, Alec Radford, Ilya Sutskever:
Distribution Augmentation for Generative Modeling. ICML 2020: 5006-5019 - [c43]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 - [i36]Prafulla Dhariwal, Heewoo Jun, Christine Payne, Jong Wook Kim, Alec Radford, Ilya Sutskever:
Jukebox: A Generative Model for Music. CoRR abs/2005.00341 (2020) - [i35]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 abs/2005.14165 (2020) - [i34]Stanislas Polu
, Ilya Sutskever:
Generative Language Modeling for Automated Theorem Proving. CoRR abs/2009.03393 (2020)
2010 – 2019
- 2019
- [c42]Will Grathwohl, Ricky T. Q. Chen, Jesse Bettencourt, Ilya Sutskever, David Duvenaud:
FFJORD: Free-Form Continuous Dynamics for Scalable Reversible Generative Models. ICLR 2019 - [c41]Daniel Huang, Prafulla Dhariwal, Dawn Song, Ilya Sutskever:
GamePad: A Learning Environment for Theorem Proving. ICLR (Poster) 2019 - [i33]Rewon Child, Scott Gray, Alec Radford, Ilya Sutskever:
Generating Long Sequences with Sparse Transformers. CoRR abs/1904.10509 (2019) - [i32]Preetum Nakkiran, Gal Kaplun, Yamini Bansal, Tristan Yang, Boaz Barak, Ilya Sutskever:
Deep Double Descent: Where Bigger Models and More Data Hurt. CoRR abs/1912.02292 (2019) - [i31]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 abs/1912.06680 (2019) - 2018
- [c40]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 - [c39]Trapit Bansal, Jakub Pachocki, Szymon Sidor, Ilya Sutskever, Igor Mordatch:
Emergent Complexity via Multi-Agent Competition. ICLR (Poster) 2018 - [c38]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: 9300-9310 - [i30]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 abs/1803.01118 (2018) - [i29]Daniel Huang, Prafulla Dhariwal, Dawn Song, Ilya Sutskever:
GamePad: A Learning Environment for Theorem Proving. CoRR abs/1806.00608 (2018) - [i28]Will Grathwohl, Ricky T. Q. Chen, Jesse Bettencourt, Ilya Sutskever, David Duvenaud:
FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models. CoRR abs/1810.01367 (2018) - 2017
- [j5]Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton:
ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6): 84-90 (2017) - [c37]Yuping Luo, Chung-Cheng Chiu, Navdeep Jaitly, Ilya Sutskever:
Learning online alignments with continuous rewards policy gradient. ICASSP 2017: 2801-2805 - [c36]Xi Chen, Diederik P. Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, Pieter Abbeel:
Variational Lossy Autoencoder. ICLR (Poster) 2017 - [c35]Bradly C. Stadie, Pieter Abbeel, Ilya Sutskever:
Third Person Imitation Learning. ICLR (Poster) 2017 - [c34]Yan Duan, Marcin Andrychowicz, Bradly C. Stadie, Jonathan Ho, Jonas Schneider, Ilya Sutskever, Pieter Abbeel, Wojciech Zaremba:
One-Shot Imitation Learning. NIPS 2017: 1087-1098 - [i27]Bradly C. Stadie, Pieter Abbeel, Ilya Sutskever:
Third-Person Imitation Learning. CoRR abs/1703.01703 (2017) - [i26]Tim Salimans, Jonathan Ho, Xi Chen, Ilya Sutskever:
Evolution Strategies as a Scalable Alternative to Reinforcement Learning. CoRR abs/1703.03864 (2017) - [i25]Yan Duan, Marcin Andrychowicz, Bradly C. Stadie, Jonathan Ho, Jonas Schneider, Ilya Sutskever, Pieter Abbeel, Wojciech Zaremba:
One-Shot Imitation Learning. CoRR abs/1703.07326 (2017) - [i24]Alec Radford, Rafal Józefowicz, Ilya Sutskever:
Learning to Generate Reviews and Discovering Sentiment. CoRR abs/1704.01444 (2017) - [i23]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 abs/1706.06428 (2017) - [i22]Maruan Al-Shedivat, Trapit Bansal, Yuri Burda, Ilya Sutskever, Igor Mordatch, Pieter Abbeel:
Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments. CoRR abs/1710.03641 (2017) - [i21]Trapit Bansal, Jakub Pachocki, Szymon Sidor, Ilya Sutskever, Igor Mordatch:
Emergent Complexity via Multi-Agent Competition. CoRR abs/1710.03748 (2017) - 2016
- [j4]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): 484-489 (2016) - [c33]Shixiang Gu, Timothy P. Lillicrap, Ilya Sutskever, Sergey Levine:
Continuous Deep Q-Learning with Model-based Acceleration. ICML 2016: 2829-2838 - [c32]Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, Pieter Abbeel:
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets. NIPS 2016: 2172-2180 - [c31]Diederik P. Kingma, Tim Salimans, Rafal Józefowicz, Xi Chen, Ilya Sutskever, Max Welling:
Improving Variational Autoencoders with Inverse Autoregressive Flow. NIPS 2016: 4736-4744 - [c30]Navdeep Jaitly, Quoc V. Le, Oriol Vinyals, Ilya Sutskever, David Sussillo, Samy Bengio:
An Online Sequence-to-Sequence Model Using Partial Conditioning. NIPS 2016: 5067-5075 - [c29]Shixiang Gu, Sergey Levine, Ilya Sutskever, Andriy Mnih:
MuProp: Unbiased Backpropagation for Stochastic Neural Networks. ICLR (Poster) 2016 - [c28]Lukasz Kaiser, Ilya Sutskever:
Neural GPUs Learn Algorithms. ICLR (Poster) 2016 - [c27]Karol Kurach, Marcin Andrychowicz, Ilya Sutskever:
Neural Random-Access Machines. ICLR (Poster) 2016 - [c26]Minh-Thang Luong, Quoc V. Le, Ilya Sutskever, Oriol Vinyals, Lukasz Kaiser:
Multi-task Sequence to Sequence Learning. ICLR (Poster) 2016 - [c25]Arvind Neelakantan, Quoc V. Le, Ilya Sutskever:
Neural Programmer: Inducing Latent Programs with Gradient Descent. ICLR (Poster) 2016 - [i20]Shixiang Gu, Timothy P. Lillicrap, Ilya Sutskever, Sergey Levine:
Continuous Deep Q-Learning with Model-based Acceleration. CoRR abs/1603.00748 (2016) - [i19]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 abs/1603.04467 (2016) - [i18]Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, Pieter Abbeel:
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets. CoRR abs/1606.03657 (2016) - [i17]Yuping Luo, Chung-Cheng Chiu, Navdeep Jaitly, Ilya Sutskever:
Learning Online Alignments with Continuous Rewards Policy Gradient. CoRR abs/1608.01281 (2016) - [i16]Eric Price, Wojciech Zaremba, Ilya Sutskever:
Extensions and Limitations of the Neural GPU. CoRR abs/1611.00736 (2016) - [i15]Xi Chen, Diederik P. Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, Pieter Abbeel:
Variational Lossy Autoencoder. CoRR abs/1611.02731 (2016) - [i14]Yan Duan, John Schulman, Xi Chen, Peter L. Bartlett, Ilya Sutskever, Pieter Abbeel:
RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning. CoRR abs/1611.02779 (2016) - [i13]Karol Kurach, Marcin Andrychowicz, Ilya Sutskever:
Neural Random Access Machines. ERCIM News 2016(107) (2016) - 2015
- [c24]Thang Luong, Ilya Sutskever, Quoc V. Le, Oriol Vinyals, Wojciech Zaremba:
Addressing the Rare Word Problem in Neural Machine Translation. ACL (1) 2015: 11-19 - [c23]Rafal Józefowicz, Wojciech Zaremba, Ilya Sutskever:
An Empirical Exploration of Recurrent Network Architectures. ICML 2015: 2342-2350 - [c22]Oriol Vinyals, Lukasz Kaiser, Terry Koo, Slav Petrov, Ilya Sutskever, Geoffrey E. Hinton:
Grammar as a Foreign Language. NIPS 2015: 2773-2781 - [c21]Chris J. Maddison, Aja Huang, Ilya Sutskever, David Silver:
Move Evaluation in Go Using Deep Convolutional Neural Networks. ICLR (Poster) 2015 - [i12]Wojciech Zaremba, Ilya Sutskever:
Reinforcement Learning Neural Turing Machines. CoRR abs/1505.00521 (2015) - [i11]Navdeep Jaitly, Quoc V. Le, Oriol Vinyals, Ilya Sutskever, Samy Bengio:
An Online Sequence-to-Sequence Model Using Partial Conditioning. CoRR abs/1511.04868 (2015) - [i10]Ilya Sutskever, Rafal Józefowicz, Karol Gregor, Danilo Jimenez Rezende, Timothy P. Lillicrap, Oriol Vinyals:
Towards Principled Unsupervised Learning. CoRR abs/1511.06440 (2015) - [i9]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 abs/1511.06807 (2015) - 2014
- [j3]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): 1929-1958 (2014) - [c20]Ilya Sutskever, Oriol Vinyals, Quoc V. Le:
Sequence to Sequence Learning with Neural Networks. NIPS 2014: 3104-3112 - [c19]David Eigen, Marc'Aurelio Ranzato, Ilya Sutskever:
Learning Factored Representations in a Deep Mixture of Experts. ICLR (Workshop Poster) 2014 - [c18]Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. Goodfellow, Rob Fergus:
Intriguing properties of neural networks. ICLR (Poster) 2014 - [i8]Wojciech Zaremba, Ilya Sutskever, Oriol Vinyals:
Recurrent Neural Network Regularization. CoRR abs/1409.2329 (2014) - [i7]Ilya Sutskever, Oriol Vinyals, Quoc V. Le:
Sequence to Sequence Learning with Neural Networks. CoRR abs/1409.3215 (2014) - [i6]Wojciech Zaremba, Ilya Sutskever:
Learning to Execute. CoRR abs/1410.4615 (2014) - [i5]Thang Luong, Ilya Sutskever, Quoc V. Le, Oriol Vinyals, Wojciech Zaremba:
Addressing the Rare Word Problem in Neural Machine Translation. CoRR abs/1410.8206 (2014) - [i4]Oriol Vinyals, Lukasz Kaiser, Terry Koo, Slav Petrov, Ilya Sutskever, Geoffrey E. Hinton:
Grammar as a Foreign Language. CoRR abs/1412.7449 (2014) - 2013
- [b1]Ilya Sutskever:
Training Recurrent Neural Networks. University of Toronto, Canada, 2013 - [c17]Daniel Tarlow, Kevin Swersky, Laurent Charlin, Ilya Sutskever, Richard S. Zemel:
Stochastic k-Neighborhood Selection for Supervised and Unsupervised Learning. ICML (3) 2013: 199-207 - [c16]Ilya Sutskever, James Martens, George E. Dahl, Geoffrey E. Hinton:
On the importance of initialization and momentum in deep learning. ICML (3) 2013: 1139-1147 - [c15]Tomás Mikolov, Ilya Sutskever, Kai Chen, Gregory S. Corrado, Jeffrey Dean:
Distributed Representations of Words and Phrases and their Compositionality. NIPS 2013: 3111-3119 - [i3]Tomás Mikolov, Quoc V. Le, Ilya Sutskever:
Exploiting Similarities among Languages for Machine Translation. CoRR abs/1309.4168 (2013) - [i2]Tomás Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, Jeffrey Dean:
Distributed Representations of Words and Phrases and their Compositionality. CoRR abs/1310.4546 (2013) - 2012
- [c14]James Martens, Ilya Sutskever, Kevin Swersky:
Estimating the Hessian by Back-propagating Curvature. ICML 2012 - [c13]Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton:
ImageNet Classification with Deep Convolutional Neural Networks. NIPS 2012: 1106-1114 - [c12]Kevin Swersky, Daniel Tarlow, Ilya Sutskever, Ruslan Salakhutdinov, Richard S. Zemel, Ryan P. Adams:
Cardinality Restricted Boltzmann Machines. NIPS 2012: 3302-3310 - [p1]James Martens, Ilya Sutskever:
Training Deep and Recurrent Networks with Hessian-Free Optimization. Neural Networks: Tricks of the Trade (2nd ed.) 2012: 479-535 - [i1]Geoffrey E. Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov:
Improving neural networks by preventing co-adaptation of feature detectors. CoRR abs/1207.0580 (2012) - 2011
- [c11]Ilya Sutskever, James Martens, Geoffrey E. Hinton:
Generating Text with Recurrent Neural Networks. ICML 2011: 1017-1024 - [c10]James Martens, Ilya Sutskever:
Learning Recurrent Neural Networks with Hessian-Free Optimization. ICML 2011: 1033-1040 - 2010
- [j2]Ilya Sutskever, Geoffrey E. Hinton:
Temporal-Kernel Recurrent Neural Networks. Neural Networks 23(2): 239-243 (2010) - [c9]James Martens, Ilya Sutskever:
Parallelizable Sampling of Markov Random Fields. AISTATS 2010: 517-524 - [c8]Ilya Sutskever, Tijmen Tieleman:
On the Convergence Properties of Contrastive Divergence. AISTATS 2010: 789-795
2000 – 2009
- 2009
- [c7]Ilya Sutskever:
A simpler unified analysis of budget perceptrons. ICML 2009: 985-992 - [c6]Ilya Sutskever, Ruslan Salakhutdinov, Joshua B. Tenenbaum:
Modelling Relational Data using Bayesian Clustered Tensor Factorization. NIPS 2009: 1821-1828 - 2008
- [j1]Ilya Sutskever, Geoffrey E. Hinton:
Deep, Narrow Sigmoid Belief Networks Are Universal Approximators. Neural Comput. 20(11): 2629-2636 (2008) - [c5]Ilya Sutskever, Vinod Nair:
Mimicking Go Experts with Convolutional Neural Networks. ICANN (2) 2008: 101-110 - [c4]Ilya Sutskever, Geoffrey E. Hinton:
Using matrices to model symbolic relationship. NIPS 2008: 1593-1600 - [c3]Ilya Sutskever, Geoffrey E. Hinton, Graham W. Taylor:
The Recurrent Temporal Restricted Boltzmann Machine. NIPS 2008: 1601-1608 - 2007
- [c2]James Cook, Ilya Sutskever, Andriy Mnih, Geoffrey E. Hinton:
Visualizing Similarity Data with a Mixture of Maps. AISTATS 2007: 67-74 - [c1]Ilya Sutskever, Geoffrey E. Hinton:
Learning Multilevel Distributed Representations for High-Dimensional Sequences. AISTATS 2007: 548-555
Coauthor Index
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