| Imagenet classification with deep convolutional neural networks A Krizhevsky, I Sutskever, GE Hinton Advances in neural information processing systems 25, 1097-1105, 2012 | 84328 | 2012 |
| Tensorflow: Large-scale machine learning on heterogeneous distributed systems M Abadi, A Agarwal, P Barham, E Brevdo, Z Chen, C Citro, GS Corrado, ... arXiv preprint arXiv:1603.04467, 2016 | 27650* | 2016 |
| Dropout: a simple way to prevent neural networks from overfitting N Srivastava, G Hinton, A Krizhevsky, I Sutskever, R Salakhutdinov The journal of machine learning research 15 (1), 1929-1958, 2014 | 27219 | 2014 |
| Distributed representations of words and phrases and their compositionality T Mikolov, I Sutskever, K Chen, G Corrado, J Dean arXiv preprint arXiv:1310.4546, 2013 | 26704 | 2013 |
| Sequence to sequence learning with neural networks I Sutskever, O Vinyals, QV Le arXiv preprint arXiv:1409.3215, 2014 | 14479 | 2014 |
| Mastering the game of Go with deep neural networks and tree search D Silver, A Huang, CJ Maddison, A Guez, L Sifre, G Van Den Driessche, ... nature 529 (7587), 484-489, 2016 | 9962 | 2016 |
| Intriguing properties of neural networks C Szegedy, W Zaremba, I Sutskever, J Bruna, D Erhan, I Goodfellow, ... arXiv preprint arXiv:1312.6199, 2013 | 6814 | 2013 |
| Improving neural networks by preventing co-adaptation of feature detectors GE Hinton, N Srivastava, A Krizhevsky, I Sutskever, RR Salakhutdinov arXiv preprint arXiv:1207.0580, 2012 | 6330 | 2012 |
| On the importance of initialization and momentum in deep learning I Sutskever, J Martens, G Dahl, G Hinton International conference on machine learning, 1139-1147, 2013 | 3455 | 2013 |
| Infogan: Interpretable representation learning by information maximizing generative adversarial nets X Chen, Y Duan, R Houthooft, J Schulman, I Sutskever, P Abbeel arXiv preprint arXiv:1606.03657, 2016 | 2867 | 2016 |
| Improving language understanding by generative pre-training A Radford, K Narasimhan, T Salimans, I Sutskever | 1945 | 2018 |
| Recurrent neural network regularization W Zaremba, I Sutskever, O Vinyals arXiv preprint arXiv:1409.2329, 2014 | 1937 | 2014 |
| An empirical exploration of recurrent network architectures R Jozefowicz, W Zaremba, I Sutskever International conference on machine learning, 2342-2350, 2015 | 1488 | 2015 |
| Generating text with recurrent neural networks I Sutskever, J Martens, GE Hinton ICML, 2011 | 1385 | 2011 |
| Language models are unsupervised multitask learners A Radford, J Wu, R Child, D Luan, D Amodei, I Sutskever OpenAI blog 1 (8), 9, 2019 | 1261 | 2019 |
| Exploiting similarities among languages for machine translation T Mikolov, QV Le, I Sutskever arXiv preprint arXiv:1309.4168, 2013 | 1231 | 2013 |
| Improving variational inference with inverse autoregressive flow DP Kingma, T Salimans, R Jozefowicz, X Chen, I Sutskever, M Welling arXiv preprint arXiv:1606.04934, 2016 | 992 | 2016 |
| Grammar as a foreign language O Vinyals, Ł Kaiser, T Koo, S Petrov, I Sutskever, G Hinton Advances in neural information processing systems 28, 2773-2781, 2015 | 884 | 2015 |
| Evolution strategies as a scalable alternative to reinforcement learning T Salimans, J Ho, X Chen, S Sidor, I Sutskever arXiv preprint arXiv:1703.03864, 2017 | 795 | 2017 |
| Language models are few-shot learners TB Brown, B Mann, N Ryder, M Subbiah, J Kaplan, P Dhariwal, ... arXiv preprint arXiv:2005.14165, 2020 | 771 | 2020 |
Geoffrey HintonEmeritus Prof. Comp Sci, U.Toronto & Engineering Fellow, GoogleVerified email at cs.toronto.edu
