| 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 | 852 | 2023 |
| A generalist agent S Reed, K Zolna, E Parisotto, SG Colmenarejo, A Novikov, G Barth-Maron, ... arXiv preprint arXiv:2205.06175, 2022 | 743 | 2022 |
| Actor-mimic: Deep multitask and transfer reinforcement learning E Parisotto, JL Ba, R Salakhutdinov arXiv preprint arXiv:1511.06342, 2015 | 664 | 2015 |
| Generating images from captions with attention E Mansimov, E Parisotto, JL Ba, R Salakhutdinov arXiv preprint arXiv:1511.02793, 2015 | 526 | 2015 |
| The hanabi challenge: A new frontier for ai research N Bard, JN Foerster, S Chandar, N Burch, M Lanctot, HF Song, E Parisotto, ... Artificial Intelligence 280, 103216, 2020 | 392 | 2020 |
| Neuro-symbolic program synthesis E Parisotto, A Mohamed, R Singh, L Li, D Zhou, P Kohli arXiv preprint arXiv:1611.01855, 2016 | 383 | 2016 |
| Stabilizing transformers for reinforcement learning E Parisotto, F Song, J Rae, R Pascanu, C Gulcehre, S Jayakumar, ... International Conference on Machine Learning, 7487-7498, 2020 | 355 | 2020 |
| Neural map: Structured memory for deep reinforcement learning E Parisotto, R Salakhutdinov arXiv preprint arXiv:1702.08360, 2017 | 292 | 2017 |
| Efficient Exploration via State Marginal Matching L Lee, B Eysenbach, E Parisotto, E Xing, S Levine, R Salakhutdinov arXiv preprint arXiv:1906.05274, 2019 | 258 | 2019 |
| 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 | 126 | 2024 |
| Active Neural Localization DS Chaplot, E Parisotto, R Salakhutdinov arXiv preprint arXiv:1801.08214, 2018 | 107 | 2018 |
| Gated path planning networks L Lee, E Parisotto, DS Chaplot, E Xing, R Salakhutdinov International Conference on Machine Learning, 2947-2955, 2018 | 98 | 2018 |
| Global pose estimation with an attention-based recurrent network E Parisotto, D Singh Chaplot, J Zhang, R Salakhutdinov Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 90 | 2018 |
| In-context reinforcement learning with algorithm distillation M Laskin, L Wang, J Oh, E Parisotto, S Spencer, R Steigerwald, ... arXiv preprint arXiv:2210.14215, 2022 | 77 | 2022 |
| RoboCat: A Self-Improving Foundation Agent for Robotic Manipulation K Bousmalis, G Vezzani, D Rao, C Devin, AX Lee, M Bauza, T Davchev, ... arXiv preprint arXiv:2306.11706, 2023 | 52 | 2023 |
| Shaking the foundations: delusions in sequence models for interaction and control PA Ortega, M Kunesch, G Delétang, T Genewein, J Grau-Moya, J Veness, ... arXiv preprint arXiv:2110.10819, 2021 | 46 | 2021 |
| Structured state space models for in-context reinforcement learning C Lu, Y Schroecker, A Gu, E Parisotto, J Foerster, S Singh, F Behbahani Advances in Neural Information Processing Systems 36, 2024 | 41 | 2024 |
| Efficient Transformers in Reinforcement Learning using Actor-Learner Distillation E Parisotto, R Salakhutdinov arXiv preprint arXiv:2104.01655, 2021 | 38 | 2021 |
| Imitate and Repurpose: Learning Reusable Robot Movement Skills From Human and Animal Behaviors S Bohez, S Tunyasuvunakool, P Brakel, F Sadeghi, L Hasenclever, ... arXiv preprint arXiv:2203.17138, 2022 | 34 | 2022 |
| Concurrent Meta Reinforcement Learning E Parisotto, S Ghosh, SB Yalamanchi, V Chinnaobireddy, Y Wu, ... arXiv preprint arXiv:1903.02710, 2019 | 22 | 2019 |
