| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, JB Alayrac, J Yu, R Soricut, J Schalkwyk, ... arXiv preprint arXiv:2312.11805, 2023 | 6992 | 2023 |
| 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 | 6665 | 2019 |
| Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context G Team, P Georgiev, VI Lei, R Burnell, L Bai, A Gulati, G Tanzer, ... arXiv preprint arXiv:2403.05530, 2024 | 3439 | 2024 |
| Gemini 2.5: Pushing the frontier with advanced reasoning, multimodality, long context, and next generation agentic capabilities G Comanici, E Bieber, M Schaekermann, I Pasupat, N Sachdeva, I Dhillon, ... arXiv preprint arXiv:2507.06261, 2025 | 1337 | 2025 |
| 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 | 1096 | 2015 |
| Value prediction network J Oh, S Singh, H Lee Advances in neural information processing systems 30, 2017 | 443 | 2017 |
| Self-imitation learning J Oh, Y Guo, S Singh, H Lee International conference on machine learning, 3878-3887, 2018 | 421 | 2018 |
| Control of memory, active perception, and action in minecraft J Oh, V Chockalingam, H Lee International conference on machine learning, 2790-2799, 2016 | 411 | 2016 |
| 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 | 351 | 2017 |
| On learning intrinsic rewards for policy gradient methods Z Zheng, J Oh, S Singh Advances in neural information processing systems 31, 2018 | 267 | 2018 |
| 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 | 223 | 2016 |
| 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 | 214 | 2022 |
| 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 | 196 | 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 | 120 | 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 | 113 | 2020 |
| 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 | 105 | 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 | 103 | 2018 |
| 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 | 102 | 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 | 99 | 2020 |
| Deep reinforcement learning with plasticity injection E Nikishin, J Oh, G Ostrovski, C Lyle, R Pascanu, W Dabney, A Barreto Advances in Neural Information Processing Systems 36, 37142-37159, 2023 | 78 | 2023 |
Satinder SinghGoogle DeepMind / U. of MichiganVerified email at umich.edu
