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| 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 | 971 | 2023 |
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| Solving quantitative reasoning problems with language models A Lewkowycz, A Andreassen, D Dohan, E Dyer, H Michalewski, ... Advances in Neural Information Processing Systems 35, 3843-3857, 2022 | 467 | 2022 |
| Show your work: Scratchpads for intermediate computation with language models M Nye, AJ Andreassen, G Gur-Ari, H Michalewski, J Austin, D Bieber, ... arXiv preprint arXiv:2112.00114, 2021 | 441 | 2021 |
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| Reinforcement learning of theorem proving C Kaliszyk, J Urban, H Michalewski, M Olšák Advances in Neural Information Processing Systems 31, 2018 | 179 | 2018 |
| Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context M Reid, N Savinov, D Teplyashin, D Lepikhin, T Lillicrap, J Alayrac, ... arXiv preprint arXiv:2403.05530, 2024 | 174 | 2024 |
| Simulation-based reinforcement learning for real-world autonomous driving B Osiński, A Jakubowski, P Zięcina, P Miłoś, C Galias, S Homoceanu, ... 2020 IEEE international conference on robotics and automation (ICRA), 6411-6418, 2020 | 140 | 2020 |
| Learning to run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environments Ł Kidziński, SP Mohanty, CF Ong, Z Huang, S Zhou, A Pechenko, ... The NIPS'17 Competition: Building Intelligent Systems, 121-153, 2018 | 94 | 2018 |
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| Promptbreeder: Self-referential self-improvement via prompt evolution C Fernando, D Banarse, H Michalewski, S Osindero, T Rocktäschel arXiv preprint arXiv:2309.16797, 2023 | 75 | 2023 |
| Rt-2: Vision-language-action models transfer web knowledge to robotic control B Zitkovich, T Yu, S Xu, P Xu, T Xiao, F Xia, J Wu, P Wohlhart, S Welker, ... Conference on Robot Learning, 2165-2183, 2023 | 73 | 2023 |
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Piotr MiłośUniversity of Warsaw, Polish Academy of Sciences and IDEAS NCBR在 mimuw.edu.pl 的电子邮件经过验证
