| Unsupervised representation learning with deep convolutional generative adversarial networks A Radford, L Metz, S Chintala arXiv preprint arXiv:1511.06434, 2015 | 14318 | 2015 |
| Proximal policy optimization algorithms J Schulman, F Wolski, P Dhariwal, A Radford, O Klimov arXiv preprint arXiv:1707.06347, 2017 | 10680 | 2017 |
| Language Models are Unsupervised Multitask Learners A Radford, J Wu, R Child, D Luan, D Amodei, I Sutskever Technical report, OpenAi, 2019 | 9851* | 2019 |
| 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 | 8129 | 2020 |
| Improved techniques for training gans T Salimans, I Goodfellow, W Zaremba, V Cheung, A Radford, X Chen Advances in neural information processing systems 29, 2016 | 7865 | 2016 |
| Improving language understanding by generative pre-training A Radford, K Narasimhan, T Salimans, I Sutskever | 5453 | 2018 |
| Learning Transferable Visual Models From Natural Language Supervision A Radford, JW Kim, C Hallacy, A Ramesh, G Goh, S Agarwal, G Sastry, ... https://cdn.openai.com/papers …, 2021 | 3702 | 2021 |
| 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 | 1213 | 2021 |
| Generating long sequences with sparse transformers R Child, S Gray, A Radford, I Sutskever arXiv preprint arXiv:1904.10509, 2019 | 935 | 2019 |
| Openai baselines P Dhariwal, C Hesse, O Klimov, A Nichol, M Plappert, A Radford, ... | 892 | 2017 |
| Generative pretraining from pixels M Chen, A Radford, R Child, J Wu, H Jun, D Luan, I Sutskever International conference on machine learning, 1691-1703, 2020 | 848 | 2020 |
| Stable baselines A Hill, A Raffin, M Ernestus, A Gleave, A Kanervisto, R Traore, P Dhariwal, ... | 726 | 2018 |
| Learning to generate reviews and discovering sentiment A Radford, R Jozefowicz, I Sutskever arXiv preprint arXiv:1704.01444, 2017 | 445 | 2017 |
| Evaluating large language models trained on code M Chen, J Tworek, H Jun, Q Yuan, HPO Pinto, J Kaplan, H Edwards, ... arXiv preprint arXiv:2107.03374, 2021 | 404 | 2021 |
| Scaling laws for neural language models J Kaplan, S McCandlish, T Henighan, TB Brown, B Chess, R Child, ... arXiv preprint arXiv:2001.08361, 2020 | 371 | 2020 |
| Jukebox: A generative model for music P Dhariwal, H Jun, C Payne, JW Kim, A Radford, I Sutskever arXiv preprint arXiv:2005.00341, 2020 | 338 | 2020 |
| Improving GANs using optimal transport T Salimans, H Zhang, A Radford, D Metaxas arXiv preprint arXiv:1803.05573, 2018 | 258 | 2018 |
| Fine-tuning language models from human preferences DM Ziegler, N Stiennon, J Wu, TB Brown, A Radford, D Amodei, ... arXiv preprint arXiv:1909.08593, 2019 | 203 | 2019 |
| Learning to summarize with human feedback N Stiennon, L Ouyang, J Wu, D Ziegler, R Lowe, C Voss, A Radford, ... Advances in Neural Information Processing Systems 33, 3008-3021, 2020 | 190 | 2020 |
| Better language models and their implications A Radford, J Wu, D Amodei, D Amodei, J Clark, M Brundage, I Sutskever OpenAI Blog https://openai. com/blog/better-language-models 1 (2), 2019 | 171 | 2019 |
Ilya SutskeverCo-Founder and Chief Scientist of OpenAI在 openai.com 的电子邮件经过验证
