| Imagenet classification with deep convolutional neural networks A Krizhevsky, I Sutskever, GE Hinton Advances in neural information processing systems 25, 2012 | 164014* | 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 | 56391* | 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 | 52847 | 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 | 45087 | 2013 |
| Language models are few-shot learners TB Brown arXiv preprint arXiv:2005.14165, 2020 | 32436 | 2020 |
| Sequence to Sequence Learning with Neural Networks I Sutskever arXiv preprint arXiv:1409.3215, 2014 | 27623 | 2014 |
| 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 | 21950 | 2021 |
| 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 | 20006 | 2016 |
| Intriguing properties of neural networks C Szegedy arXiv preprint arXiv:1312.6199, 2013 | 17594 | 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 | 13611 | 2019 |
| Improving neural networks by preventing co-adaptation of feature detectors GE Hinton arXiv preprint arXiv:1207.0580, 2012 | 11598 | 2012 |
| Improving language understanding by generative pre-training A Radford | 10939 | 2018 |
| 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 | 6711* | 2016 |
| 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 | 6439 | 2013 |
| 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 | 4575 | 2021 |
| Gpt-4 technical report J Achiam, S Adler, S Agarwal, L Ahmad, I Akkaya, FL Aleman, D Almeida, ... arXiv preprint arXiv:2303.08774, 2023 | 3852 | 2023 |
| Recurrent neural network regularization W Zaremba arXiv preprint arXiv:1409.2329, 2014 | 3779 | 2014 |
| Evaluating large language models trained on code M Chen, J Tworek, H Jun, Q Yuan, HPDO Pinto, J Kaplan, H Edwards, ... arXiv preprint arXiv:2107.03374, 2021 | 2879 | 2021 |
| Glide: Towards photorealistic image generation and editing with text-guided diffusion models A Nichol, P Dhariwal, A Ramesh, P Shyam, P Mishkin, B McGrew, ... arXiv preprint arXiv:2112.10741, 2021 | 2808 | 2021 |
| Robust speech recognition via large-scale weak supervision A Radford, JW Kim, T Xu, G Brockman, C McLeavey, I Sutskever International conference on machine learning, 28492-28518, 2023 | 2785 | 2023 |
Geoffrey HintonEmeritus Prof. Computer Science, University of TorontoVerified email at cs.toronto.edu
