| Grandmaster level in StarCraft II using multi-agent reinforcement learning O Vinyals, I Babuschkin, WM Czarnecki, M Mathieu, A Dudzik, J Chung, ... nature 575 (7782), 350-354, 2019 | 5218* | 2019 |
| 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 | 1605 | 2023 |
| Action-conditional video prediction using deep networks in atari games J Oh, X Guo, H Lee, RL Lewis, S Singh Advances in neural information processing systems 28, 2015 | 1025 | 2015 |
| 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 | 411 | 2024 |
| Value prediction network J Oh, S Singh, H Lee Advances in neural information processing systems 30, 2017 | 397 | 2017 |
| Control of memory, active perception, and action in minecraft J Oh, V Chockalingam, H Lee International conference on machine learning, 2790-2799, 2016 | 373 | 2016 |
| Self-imitation learning J Oh, Y Guo, S Singh, H Lee International conference on machine learning, 3878-3887, 2018 | 361 | 2018 |
| Zero-shot task generalization with multi-task deep reinforcement learning J Oh, S Singh, H Lee, P Kohli International Conference on Machine Learning, 2661-2670, 2017 | 319 | 2017 |
| Learning transferrable knowledge for semantic segmentation with deep convolutional neural network S Hong, J Oh, H Lee, B Han Proceedings of the IEEE conference on computer vision and pattern …, 2016 | 221 | 2016 |
| On learning intrinsic rewards for policy gradient methods Z Zheng, J Oh, S Singh Advances in Neural Information Processing Systems 31, 2018 | 212 | 2018 |
| Discovering reinforcement learning algorithms J Oh, M Hessel, WM Czarnecki, Z Xu, HP van Hasselt, S Singh, D Silver Advances in Neural Information Processing Systems 33, 1060-1070, 2020 | 154 | 2020 |
| Hierarchical reinforcement learning for zero-shot generalization with subtask dependencies S Sohn, J Oh, H Lee Advances in neural information processing systems 31, 2018 | 106 | 2018 |
| Discovery of useful questions as auxiliary tasks V Veeriah, M Hessel, Z Xu, J Rajendran, RL Lewis, J Oh, HP van Hasselt, ... Advances in Neural Information Processing Systems 32, 2019 | 95 | 2019 |
| Contingency-aware exploration in reinforcement learning J Choi, Y Guo, M Moczulski, J Oh, N Wu, M Norouzi, H Lee arXiv preprint arXiv:1811.01483, 2018 | 94 | 2018 |
| What can learned intrinsic rewards capture? Z Zheng, J Oh, M Hessel, Z Xu, M Kroiss, H Van Hasselt, D Silver, S Singh International Conference on Machine Learning, 11436-11446, 2020 | 92 | 2020 |
| 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 | 89 | 2022 |
| A self-tuning actor-critic algorithm T Zahavy, Z Xu, V Veeriah, M Hessel, J Oh, HP van Hasselt, D Silver, ... Advances in neural information processing systems 33, 20913-20924, 2020 | 86 | 2020 |
| Meta-gradient reinforcement learning with an objective discovered online Z Xu, HP van Hasselt, M Hessel, J Oh, S Singh, D Silver Advances in Neural Information Processing Systems 33, 15254-15264, 2020 | 78 | 2020 |
| Generative adversarial self-imitation learning Y Guo, J Oh, S Singh, H Lee arXiv preprint arXiv:1812.00950, 2018 | 62 | 2018 |
| Many-goals reinforcement learning V Veeriah, J Oh, S Singh arXiv preprint arXiv:1806.09605, 2018 | 57 | 2018 |
Satinder SinghGoogle DeepMind / U. of MichiganEmail verificata su umich.edu
