| 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 | 2453 | 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 | 902 | 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 | 299 | 2024 |
| 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 | 275 | 2021 |
| 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 |
| What matters in on-policy reinforcement learning? a large-scale empirical study M Andrychowicz, A Raichuk, P Stańczyk, M Orsini, S Girgin, R Marinier, ... arXiv preprint arXiv:2006.05990, 2020 | 258 | 2020 |
| What matters for on-policy deep actor-critic methods? a large-scale study M Andrychowicz, A Raichuk, P Stańczyk, M Orsini, S Girgin, R Marinier, ... International conference on learning representations, 2021 | 207 | 2021 |
| Speak, read and prompt: High-fidelity text-to-speech with minimal supervision E Kharitonov, D Vincent, Z Borsos, R Marinier, S Girgin, O Pietquin, ... Transactions of the Association for Computational Linguistics 11, 1703-1718, 2023 | 178 | 2023 |
| Swarm robotics E Şahin, S Girgin, L Bayindir, AE Turgut Swarm intelligence: introduction and applications, 87-100, 2008 | 133 | 2008 |
| Nash learning from human feedback R Munos, M Valko, D Calandriello, MG Azar, M Rowland, ZD Guo, Y Tang, ... arXiv preprint arXiv:2312.00886, 2023 | 88 | 2023 |
| What matters for adversarial imitation learning? M Orsini, A Raichuk, L Hussenot, D Vincent, R Dadashi, S Girgin, M Geist, ... Advances in Neural Information Processing Systems 34, 14656-14668, 2021 | 82 | 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, ... arXiv preprint arXiv:2306.00186, 2023 | 73 | 2023 |
| Brax-a differentiable physics engine for large scale rigid body simulation, 2021 CD Freeman, E Frey, A Raichuk, S Girgin, I Mordatch, O Bachem URL http://github. com/google/brax 6, 2021 | 73 | 2021 |
| Scalable deep reinforcement learning algorithms for mean field games M Laurière, S Perrin, S Girgin, P Muller, A Jain, T Cabannes, G Piliouras, ... International Conference on Machine Learning, 12078-12095, 2022 | 57 | 2022 |
| What matters in on-policy reinforcement learning M Andrychowicz, A Raichuk, P Stanczyk, M Orsini, S Girgin, R Marinier, ... A large-scale empirical study. CoRR, abs/2006.05990 3, 2020 | 37 | 2020 |
| Continuous control with action quantization from demonstrations R Dadashi, L Hussenot, D Vincent, S Girgin, A Raichuk, M Geist, ... arXiv preprint arXiv:2110.10149, 2021 | 36 | 2021 |
| Improving reinforcement learning by using sequence trees S Girgin, F Polat, R Alhajj Machine learning 81, 283-331, 2010 | 36 | 2010 |
| Feature discovery in reinforcement learning using genetic programming S Girgin, P Preux European conference on genetic programming, 218-229, 2008 | 32 | 2008 |
| A novel report generation approach for medical applications: the SISDS methodology and its applications K Kuru, S Girgin, K Arda, U Bozlar International journal of medical informatics 82 (5), 435-447, 2013 | 31 | 2013 |
| Solving N-player dynamic routing games with congestion: a mean field approach T Cabannes, M Lauriere, J Perolat, R Marinier, S Girgin, S Perrin, ... arXiv preprint arXiv:2110.11943, 2021 | 25 | 2021 |
