| Unsupervised representation learning with deep convolutional generative adversarial networks A Radford arXiv preprint arXiv:1511.06434, 2015 | 19464 | 2015 |
| Gpt-4 technical report J Achiam, S Adler, S Agarwal, L Ahmad, I Akkaya, FL Aleman, D Almeida, ... arXiv preprint arXiv:2303.08774, 2023 | 7842 | 2023 |
| BEGAN: Boundary Equilibrium Generative Adversarial Networks D Berthelot arXiv preprint arXiv:1703.10717, 2017 | 1557 | 2017 |
| Unrolled generative adversarial networks L Metz, B Poole, D Pfau, J Sohl-Dickstein arXiv preprint arXiv:1611.02163, 2016 | 1349 | 2016 |
| Beyond the imitation game: Quantifying and extrapolating the capabilities of language models A Srivastava, A Rastogi, A Rao, AAM Shoeb, A Abid, A Fisch, AR Brown, ... arXiv preprint arXiv:2206.04615, 2022 | 1293 | 2022 |
| Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv 2015 A Radford, L Metz, S Chintala arXiv preprint arXiv:1511.06434, 2015 | 664 | 2015 |
| Adversarial spheres J Gilmer, L Metz, F Faghri, SS Schoenholz, M Raghu, M Wattenberg, ... arXiv preprint arXiv:1801.02774, 2018 | 422 | 2018 |
| ChatGPT: Optimizing language models for dialogue J Schulman, B Zoph, C Kim, J Hilton, J Menick, J Weng, JFC Uribe, ... OpenAI blog 2 (4), 2022 | 322 | 2022 |
| Understanding and correcting pathologies in the training of learned optimizers L Metz, N Maheswaranathan, J Nixon, D Freeman, J Sohl-Dickstein International Conference on Machine Learning, 4556-4565, 2019 | 166 | 2019 |
| Meta-learning update rules for unsupervised representation learning L Metz, N Maheswaranathan, B Cheung, J Sohl-Dickstein arXiv preprint arXiv:1804.00222, 2018 | 146 | 2018 |
| Discrete sequential prediction of continuous actions for deep rl L Metz, J Ibarz, N Jaitly, J Davidson arXiv preprint arXiv:1705.05035, 2017 | 145 | 2017 |
| Gpt-4o system card A Hurst, A Lerer, AP Goucher, A Perelman, A Ramesh, A Clark, AJ Ostrow, ... arXiv preprint arXiv:2410.21276, 2024 | 129 | 2024 |
| Guided evolutionary strategies: Augmenting random search with surrogate gradients N Maheswaranathan, L Metz, G Tucker, D Choi, J Sohl-Dickstein International Conference on Machine Learning, 4264-4273, 2019 | 104 | 2019 |
| Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv e-prints A Radford, L Metz, S Chintala arXiv preprint arXiv:1511.06434 1511, 2015 | 95 | 2015 |
| On linear identifiability of learned representations G Roeder, L Metz, D Kingma International Conference on Machine Learning, 9030-9039, 2021 | 91 | 2021 |
| Gradients are not all you need L Metz, CD Freeman, SS Schoenholz, T Kachman arXiv preprint arXiv:2111.05803, 2021 | 88 | 2021 |
| Discovered policy optimisation C Lu, J Kuba, A Letcher, L Metz, C Schroeder de Witt, J Foerster Advances in Neural Information Processing Systems 35, 16455-16468, 2022 | 83 | 2022 |
| Learning an adaptive learning rate schedule Z Xu, AM Dai, J Kemp, L Metz arXiv preprint arXiv:1909.09712, 2019 | 78 | 2019 |
| Velo: Training versatile learned optimizers by scaling up L Metz, J Harrison, CD Freeman, A Merchant, L Beyer, J Bradbury, ... arXiv preprint arXiv:2211.09760, 2022 | 72 | 2022 |
| General-purpose in-context learning by meta-learning transformers L Kirsch, J Harrison, J Sohl-Dickstein, L Metz arXiv preprint arXiv:2212.04458, 2022 | 71 | 2022 |