| Imagenet classification with deep convolutional neural networks A Krizhevsky, I Sutskever, GE Hinton Advances in neural information processing systems 25, 2012 | 142319* | 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 | 48582* | 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 | 46067 | 2014 |
| 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 26, 2013 | 40690 | 2013 |
| Sequence to sequence learning with neural networks I Sutskever, O Vinyals, QV Le Advances in neural information processing systems 27, 2014 | 23600 | 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 | 16935 | 2016 |
| Language models are few-shot learners T Brown, B Mann, N Ryder, M Subbiah, JD Kaplan, P Dhariwal, ... Advances in neural information processing systems 33, 1877-1901, 2020 | 14631 | 2020 |
| Intriguing properties of neural networks C Szegedy, W Zaremba, I Sutskever, J Bruna, D Erhan, I Goodfellow, ... arXiv preprint arXiv:1312.6199, 2013 | 14315 | 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 | 10544 | 2012 |
| Learning transferable visual models from natural language supervision A Radford, JW Kim, C Hallacy, A Ramesh, G Goh, S Agarwal, G Sastry, ... International conference on machine learning, 8748-8763, 2021 | 8078 | 2021 |
| Language models are unsupervised multitask learners A Radford, J Wu, R Child, D Luan, D Amodei, I Sutskever OpenAI blog 1 (8), 9, 2019 | 6930 | 2019 |
| Improving language understanding by generative pre-training A Radford, K Narasimhan, T Salimans, I Sutskever | 6624 | 2018 |
| 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 | 5648 | 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 29, 2016 | 5595* | 2016 |
| Recurrent neural network regularization W Zaremba, I Sutskever, O Vinyals arXiv preprint arXiv:1409.2329, 2014 | 3268 | 2014 |
| Zero-shot text-to-image generation A Ramesh, M Pavlov, G Goh, S Gray, C Voss, A Radford, M Chen, ... International Conference on Machine Learning, 8821-8831, 2021 | 2279 | 2021 |
| An empirical exploration of recurrent network architectures R Jozefowicz, W Zaremba, I Sutskever International conference on machine learning, 2342-2350, 2015 | 2226 | 2015 |
| Generating text with recurrent neural networks I Sutskever, J Martens, GE Hinton Proceedings of the 28th international conference on machine learning (ICML …, 2011 | 1895 | 2011 |
| 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 29, 2016 | 1806 | 2016 |
| Exploiting similarities among languages for machine translation T Mikolov, QV Le, I Sutskever arXiv preprint arXiv:1309.4168, 2013 | 1751 | 2013 |
Geoffrey HintonEmeritus Prof. Computer Science, University of TorontoVerified email at cs.toronto.edu
