| Unsupervised representation learning with deep convolutional generative adversarial networks A Radford, L Metz, S Chintala ICLR 2016, 2015 | 8470 | 2015 |
| Began: Boundary equilibrium generative adversarial networks D Berthelot, T Schumm, L Metz arXiv preprint arXiv:1703.10717, 2017 | 915 | 2017 |
| Unrolled generative adversarial networks L Metz, B Poole, D Pfau, J Sohl-Dickstein arXiv preprint arXiv:1611.02163, 2016 | 643 | 2016 |
| Adversarial spheres J Gilmer, L Metz, F Faghri, SS Schoenholz, M Raghu, M Wattenberg, ... arXiv preprint arXiv:1801.02774, 2018 | 218 | 2018 |
| Meta-Learning Update Rules for Unsupervised Representation Learning L Metz, N Maheswaranathan, B Cheung, J Sohl-Dickstein ICLR 2019, 2018 | 67* | 2018 |
| Discrete Sequential Prediction of Continuous Actions for Deep RL L Metz, J Ibarz, N Jaitly, J Davidson arXiv preprint arXiv:1705.05035, 2017 | 51 | 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, 2019 | 39* | 2019 |
| 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 | 28* | 2019 |
| Towards GAN Benchmarks Which Require Generalization I Gulrajani, C Raffel, L Metz ICLR 2019, 2018 | 18 | 2018 |
| Learning to predict without looking ahead: World models without forward prediction CD Freeman, L Metz, D Ha arXiv preprint arXiv:1910.13038, 2019 | 10 | 2019 |
| Using learned optimizers to make models robust to input noise L Metz, N Maheswaranathan, J Shlens, J Sohl-Dickstein, ED Cubuk ICML 2019 Workshop on Uncertainty and Robustness in Deep Learning, 2019 | 9 | 2019 |
| Learning an adaptive learning rate schedule Z Xu, AM Dai, J Kemp, L Metz arXiv preprint arXiv:1909.09712, 2019 | 6 | 2019 |
| Using a thousand optimization tasks to learn hyperparameter search strategies L Metz, N Maheswaranathan, R Sun, CD Freeman, B Poole, ... arXiv preprint arXiv:2002.11887, 2020 | 5 | 2020 |
| On linear identifiability of learned representations G Roeder, L Metz, DP Kingma arXiv preprint arXiv:2007.00810, 2020 | 3 | 2020 |
| Meta-learning biologically plausible semi-supervised update rules K Gu, S Greydanus, L Metz, N Maheswaranathan, J Sohl-Dickstein bioRxiv, 2019 | 3 | 2019 |
| Visualizing with t-SNE L Metz Indico. Web 25, 2015 | 3 | 2015 |
| 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 | 2 | 2020 |
| Training Learned Optimizers with Randomly Initialized Learned Optimizers L Metz, CD Freeman, N Maheswaranathan, J Sohl-Dickstein arXiv preprint arXiv:2101.07367, 2021 | | 2021 |
| Parallel Training of Deep Networks with Local Updates M Laskin, L Metz, S Nabarrao, M Saroufim, B Noune, C Luschi, ... arXiv preprint arXiv:2012.03837, 2020 | | 2020 |
| Ridge Rider: Finding Diverse Solutions by Following Eigenvectors of the Hessian J Parker-Holder, L Metz, C Resnick, H Hu, A Lerer, A Letcher, ... arXiv preprint arXiv:2011.06505, 2020 | | 2020 |
Jascha Sohl-DicksteinGoogle BrainVerified email at google.com
