| Suphx: Mastering mahjong with deep reinforcement learning J Li, S Koyamada, Q Ye, G Liu, C Wang, R Yang, L Zhao, T Qin, TY Liu, ... arXiv preprint arXiv:2003.13590, 2020 | 136 | 2020 |
| Deep learning of fMRI big data: a novel approach to subject-transfer decoding S Koyamada, Y Shikauchi, K Nakae, M Koyama, S Ishii arXiv preprint arXiv:1502.00093, 2015 | 83 | 2015 |
| Principal sensitivity analysis S Koyamada, M Koyama, K Nakae, S Ishii Pacific-Asia Conference on Knowledge Discovery and Data Mining, 621-632, 2015 | 10 | 2015 |
| Pgx: Hardware-Accelerated Parallel Game Simulators for Reinforcement Learning S Koyamada, S Okano, S Nishimori, Y Murata, K Habara, H Kita, S Ishii Advances in Neural Information Processing Systems 36, 45716-45743, 2023 | 8 | 2023 |
| Mjx: A framework for Mahjong AI research S Koyamada, K Habara, N Goto, S Okano, S Nishimori, S Ishii 2022 IEEE Conference on Games (CoG), 504-507, 2022 | 2 | 2022 |
| Neural Sequence Model Training via -divergence Minimization S Koyamada, Y Kikuchi, A Kanemura, S Maeda, S Ishii ICML Workshop on Learning to Generate Natural Language (LGNL 2017), 2017 | 1 | 2017 |
| End-to-End Policy Gradient Method for POMDPs and Explainable Agents S Nishimori, S Koyamada, S Ishii arXiv preprint arXiv:2304.09769, 2023 | | 2023 |
| Reinforcement learning of communication strategy between players of the game of Contract Bridge Y Yamaguchi, S Koyamada, K Nakae, S Ishii IEICE Technical Report; IEICE Tech. Rep. 119 (381), 131-134, 2020 | | 2020 |
