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 | 178 | 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 | 85 | 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 (NeurIPS) 36, 45716-45743, 2023 | 24 | 2023 |
Principal sensitivity analysis S Koyamada, M Koyama, K Nakae, S Ishii Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 621-632, 2015 | 10 | 2015 |
Mjx: A framework for Mahjong AI research S Koyamada, K Habara, N Goto, S Okano, S Nishimori, S Ishii IEEE Conference on Games (CoG), 504-507, 2022 | 4 | 2022 |
Construction of subject-independent brain decoders for human fMRI with deep learning S Koyamada, Y Shikauchi, K Nakae, S Ishii International Conference on Data Mining, Internet Computing, and Big Data, 60-68, 2014 | 2 | 2014 |
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 |
A Simple, Solid, and Reproducible Baseline for Bridge Bidding AI H Kita, S Koyamada, Y Yamaguchi, S Ishii IEEE Conference on Games (CoG), 2024 | | 2024 |
A Batch Sequential Halving Algorithm without Performance Degradation S Koyamada, S Nishimori, S Ishii Reinforcement Learning Journal 5, 2218-2232, 2024 | | 2024 |
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 |