| Gemini: A family of highly capable multimodal models G Team, R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, ... arXiv preprint arXiv:2312.11805, 2023 | 2494 | 2023 |
| Gemma: Open Models Based on Gemini Research and Technology G Team, T Mesnard, C Hardin, R Dadashi, S Bhupatiraju, S Pathak, ... arXiv preprint arXiv:2403.08295, 2024 | 925 | 2024 |
| Gemma 2: Improving Open Language Models at a Practical Size G Team, M Riviere, S Pathak, PG Sessa, C Hardin, S Bhupatiraju, ... arXiv preprint arXiv:2408.00118, 2024 | 314 | 2024 |
| Acme: A research framework for distributed reinforcement learning MW Hoffman, B Shahriari, J Aslanides, G Barth-Maron, N Momchev, ... arXiv preprint arXiv:2006.00979, 2020 | 271 | 2020 |
| Primal Wasserstein Imitation Learning R Dadashi, L Hussenot, M Geist, O Pietquin International Conference on Learning Representations (ICLR), 2021 | 151 | 2021 |
| A Geometric Perspective on Optimal Representations for Reinforcement Learning M Bellemare, W Dabney, R Dadashi, A Ali Taiga, PS Castro, N Le Roux, ... Neural Information Processing Systems (NeurIPS), 2019 | 109 | 2019 |
| Statistics and Samples in Distributional Reinforcement Learning M Rowland, R Dadashi, S Kumar, R Munos, MG Bellemare, W Dabney International Conference on Machine Learning (ICML), 2019 | 108 | 2019 |
| What Matters for Adversarial Imitation Learning? M Orsini, A Raichuk, L Hussenot, D Vincent, R Dadashi, S Girgin, M Geist, ... Neural Information Processing Systems (NeurIPS), 2021 | 83 | 2021 |
| The Value-Improvement Path: Towards Better Representations for Reinforcement Learning W Dabney, A Barreto, M Rowland, R Dadashi, J Quan, MG Bellemare, ... AAAI Conference on Artificial Intelligence, 2021 | 75 | 2021 |
| Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback P Roit, J Ferret, L Shani, R Aharoni, G Cideron, R Dadashi, M Geist, ... Annual Meeting of the Association for Computational Linguistics (ACL), 2023 | 73 | 2023 |
| Offline Reinforcement Learning as Anti-Exploration S Rezaeifar*, R Dadashi*, N Vieillard, L Hussenot, O Bachem, O Pietquin, ... AAAI Conference on Artificial Intelligence, 2022 | 61 | 2022 |
| WARM: On the Benefits of Weight Averaged Reward Models A Ramé, N Vieillard, L Hussenot, R Dadashi, G Cideron, O Bachem, ... arXiv preprint arXiv:2401.12187, 2024 | 54 | 2024 |
| The Value Function Polytope in Reinforcement Learning R Dadashi, AA Taïga, NL Roux, D Schuurmans, MG Bellemare International Conference on Machine Learning (ICML), 2019 | 48 | 2019 |
| Offline Reinforcement Learning with Pseudometric Learning R Dadashi, S Rezaeifar, N Vieillard, L Hussenot, O Pietquin, M Geist International Conference on Machine Learning (ICML), 2021 | 42 | 2021 |
| Continuous Control with Action Quantization from Demonstrations R Dadashi*, L Hussenot*, D Vincent, S Girgin, A Raichuk, M Geist, ... International Conference on Machine Learning (ICML), 2022 | 36 | 2022 |
| Hyperparameter Selection for Imitation Learning L Hussenot, M Andrychowicz, D Vincent, R Dadashi, A Raichuk, ... International Conference on Machine Learning (ICML), 2021 | 22 | 2021 |
| BOND: Aligning LLMs with Best-of-N Distillation PG Sessa, R Dadashi, L Hussenot, J Ferret, N Vieillard, A Ramé, ... arXiv preprint arXiv:2407.14622, 2024 | 18 | 2024 |
| Show me the Way: Intrinsic Motivation from Demonstrations L Hussenot, R Dadashi, M Geist, O Pietquin International Conference on Autonomous Agents and Multiagent Systems (AAMAS …, 2020 | 12 | 2020 |
| WARP: On the Benefits of Weight Averaged Rewarded Policies A Ramé, J Ferret, N Vieillard, R Dadashi, L Hussenot, PL Cedoz, ... arXiv preprint arXiv:2406.16768, 2024 | 9 | 2024 |
| Learning Energy Networks with Generalized Fenchel-Young Losses M Blondel, F Llinares-López, R Dadashi, L Hussenot, M Geist Neural Information Processing Systems (NeurIPS), 2022 | 9 | 2022 |
Léonard HussenotGoogle DeepMindПодтвержден адрес электронной почты в домене google.com