| 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 | 444 | 2022 |
| Topology and geometry of half-rectified network optimization CD Freeman, J Bruna arXiv preprint arXiv:1611.01540, 2016 | 232 | 2016 |
| Brax--A Differentiable Physics Engine for Large Scale Rigid Body Simulation CD Freeman, E Frey, A Raichuk, S Girgin, I Mordatch, O Bachem arXiv preprint arXiv:2106.13281, 2021 | 177* | 2021 |
| 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 | 133 | 2019 |
| Modern approaches to exact diagonalization and selected configuration interaction with the adaptive sampling CI method NM Tubman, CD Freeman, DS Levine, D Hait, M Head-Gordon, ... Journal of chemical theory and computation 16 (4), 2139-2159, 2020 | 124 | 2020 |
| Multi-game decision transformers KH Lee, O Nachum, MS Yang, L Lee, D Freeman, S Guadarrama, ... Advances in Neural Information Processing Systems 35, 27921-27936, 2022 | 113 | 2022 |
| Gradients are not all you need L Metz, CD Freeman, SS Schoenholz, T Kachman arXiv preprint arXiv:2111.05803, 2021 | 57 | 2021 |
| 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 | 47 | 2020 |
| Learning to predict without looking ahead: World models without forward prediction D Freeman, D Ha, L Metz Advances in Neural Information Processing Systems 32, 2019 | 41 | 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 | 33 | 2020 |
| A method for the determination of speed-dependent semi-classical vector correlations from sliced image anisotropies MP Grubb, ML Warter, CD Freeman, NA West, KM Usakoski, KM Johnson, ... The Journal of chemical physics 135 (9), 2011 | 31* | 2011 |
| Relaxation dynamics of the toric code in contact with a thermal reservoir: Finite-size scaling in a low-temperature regime CD Freeman, CM Herdman, DJ Gorman, KB Whaley Physical Review B 90 (13), 134302, 2014 | 20 | 2014 |
| 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 | 18 | 2022 |
| Practical tradeoffs between memory, compute, and performance in learned optimizers L Metz, CD Freeman, J Harrison, N Maheswaranathan, J Sohl-Dickstein Conference on Lifelong Learning Agents, 142-164, 2022 | 17 | 2022 |
| Blocks Assemble! Learning to Assemble with Large-Scale Structured Reinforcement Learning S Kamyar Seyed Ghasemipour, D Freeman, B David, SS Gu, S Kataoka, ... arXiv e-prints, arXiv: 2203.13733, 2022 | 16* | 2022 |
| Engineering autonomous error correction in stabilizer codes at finite temperature CD Freeman, CM Herdman, KB Whaley http://arxiv.org/abs/1603.05005, 2016 | 15 | 2016 |
| Training learned optimizers with randomly initialized learned optimizers L Metz, CD Freeman, N Maheswaranathan, J Sohl-Dickstein arXiv preprint arXiv:2101.07367, 2021 | 12 | 2021 |
| Braxlines: Fast and interactive toolkit for rl-driven behavior engineering beyond reward maximization SS Gu, M Diaz, DC Freeman, H Furuta, SKS Ghasemipour, A Raichuk, ... arXiv preprint arXiv:2110.04686, 2021 | 10 | 2021 |
| Training optimizer neural networks J Nixon, JN Sohl-Dickstein, LS Metz, CD Freeman, N Maheswaranathan US Patent App. 16/586,220, 2020 | 8 | 2020 |
| Bi-manual manipulation and attachment via sim-to-real reinforcement learning S Kataoka, SKS Ghasemipour, D Freeman, I Mordatch arXiv preprint arXiv:2203.08277, 2022 | 7 | 2022 |
Luke MetzOpenAIVerified email at openai.com
