| Imagenet classification with deep convolutional neural networks A Krizhevsky, I Sutskever, GE Hinton Communications of the ACM 60 (6), 84-90, 2017 | 74615 | 2017 |
| 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 | 24208 | 2014 |
| 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 | 24186* | 2016 |
| Distributed representations of words and phrases and their compositionality T Mikolov, I Sutskever, K Chen, GS Corrado, J Dean Advances in neural information processing systems, 3111-3119, 2013 | 23990 | 2013 |
| Sequence to sequence learning with neural networks I Sutskever, O Vinyals, QV Le Advances in neural information processing systems, 3104-3112, 2014 | 13038 | 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 | 8895 | 2016 |
| 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 | 5813 | 2012 |
| Intriguing properties of neural networks C Szegedy, W Zaremba, I Sutskever, J Bruna, D Erhan, I Goodfellow, ... arXiv preprint arXiv:1312.6199, 2013 | 5785 | 2013 |
| 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 | 3095 | 2013 |
| Infogan: Interpretable representation learning by information maximizing generative adversarial nets X Chen, Y Duan, R Houthooft, J Schulman, I Sutskever, P Abbeel Advances in neural information processing systems, 2172-2180, 2016 | 2356 | 2016 |
| Recurrent neural network regularization W Zaremba, I Sutskever, O Vinyals arXiv preprint arXiv:1409.2329, 2014 | 1775 | 2014 |
| Improving language understanding by generative pre-training A Radford, K Narasimhan, T Salimans, I Sutskever | 1565 | 2018 |
| An empirical exploration of recurrent network architectures R Jozefowicz, W Zaremba, I Sutskever International conference on machine learning, 2342-2350, 2015 | 1369 | 2015 |
| Generating text with recurrent neural networks I Sutskever, J Martens, GE Hinton ICML, 2011 | 1280 | 2011 |
| Exploiting similarities among languages for machine translation T Mikolov, QV Le, I Sutskever arXiv preprint arXiv:1309.4168, 2013 | 1143 | 2013 |
| Language models are unsupervised multitask learners A Radford, J Wu, R Child, D Luan, D Amodei, I Sutskever OpenAI blog 1 (8), 9, 2019 | 936 | 2019 |
| Improved variational inference with inverse autoregressive flow DP Kingma, T Salimans, R Jozefowicz, X Chen, I Sutskever, M Welling Advances in neural information processing systems, 4743-4751, 2016 | 886 | 2016 |
| Grammar as a foreign language O Vinyals, Ł Kaiser, T Koo, S Petrov, I Sutskever, G Hinton Advances in neural information processing systems, 2773-2781, 2015 | 834 | 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 | 707 | 2017 |
| Addressing the rare word problem in neural machine translation MT Luong, I Sutskever, QV Le, O Vinyals, W Zaremba arXiv preprint arXiv:1410.8206, 2014 | 664 | 2014 |
Geoffrey HintonEmeritus Prof. Comp Sci, U.Toronto & Engineering Fellow, GoogleVerified email at cs.toronto.edu
