| Unsupervised representation learning with deep convolutional generative adversarial networks A Radford, L Metz, S Chintala arXiv preprint arXiv:1511.06434, 2015 | 17970 | 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 | 2294 | 2023 |
| Began: Boundary equilibrium generative adversarial networks D Berthelot, T Schumm, L Metz arXiv preprint arXiv:1703.10717, 2017 | 1500 | 2017 |
| Unrolled generative adversarial networks L Metz, B Poole, D Pfau, J Sohl-Dickstein arXiv preprint arXiv:1611.02163, 2016 | 1255 | 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 | 860 | 2022 |
| Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv 2015 A Radford, L Metz, S Chintala arXiv preprint arXiv:1511.06434, 2015 | 578 | 2015 |
| Adversarial spheres J Gilmer, L Metz, F Faghri, SS Schoenholz, M Raghu, M Wattenberg, ... arXiv preprint arXiv:1801.02774, 2018 | 397 | 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 | 193 | 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 | 144 | 2019 |
| Meta-learning update rules for unsupervised representation learning L Metz, N Maheswaranathan, B Cheung, J Sohl-Dickstein arXiv preprint arXiv:1804.00222, 2018 | 135 | 2018 |
| Discrete sequential prediction of continuous actions for deep rl L Metz, J Ibarz, N Jaitly, J Davidson arXiv preprint arXiv:1705.05035, 2017 | 116 | 2017 |
| 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 | 97 | 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 | 93 | 2015 |
| Gradients are not all you need L Metz, CD Freeman, SS Schoenholz, T Kachman arXiv preprint arXiv:2111.05803, 2021 | 76 | 2021 |
| On linear identifiability of learned representations G Roeder, L Metz, D Kingma International Conference on Machine Learning, 9030-9039, 2021 | 75 | 2021 |
| Learning an adaptive learning rate schedule Z Xu, AM Dai, J Kemp, L Metz arXiv preprint arXiv:1909.09712, 2019 | 65 | 2019 |
| Towards GAN benchmarks which require generalization I Gulrajani, C Raffel, L Metz arXiv preprint arXiv:2001.03653, 2020 | 59 | 2020 |
| Tasks, stability, architecture, and compute: Training more effective learned optimizers, and using them to train themselves L Metz, N Maheswaranathan, CD Freeman, B Poole, J Sohl-Dickstein arXiv preprint arXiv:2009.11243, 2020 | 57 | 2020 |
| Unbiased gradient estimation in unrolled computation graphs with persistent evolution strategies P Vicol, L Metz, J Sohl-Dickstein International Conference on Machine Learning, 10553-10563, 2021 | 56 | 2021 |
| General-purpose in-context learning by meta-learning transformers L Kirsch, J Harrison, J Sohl-Dickstein, L Metz arXiv preprint arXiv:2212.04458, 2022 | 55 | 2022 |
