| Model-based reinforcement learning via meta-policy optimization I Clavera, J Rothfuss, J Schulman, Y Fujita, T Asfour, P Abbeel Conference on Robot Learning, 617-629, 2018 | 305 | 2018 |
| Chainerrl: A deep reinforcement learning library Y Fujita, P Nagarajan, T Kataoka, T Ishikawa Journal of Machine Learning Research 22 (77), 1-14, 2021 | 151 | 2021 |
| A wrapped normal distribution on hyperbolic space for gradient-based learning Y Nagano, S Yamaguchi, Y Fujita, M Koyama International Conference on Machine Learning, 4693-4702, 2019 | 124 | 2019 |
| Surface-aligned neural radiance fields for controllable 3d human synthesis T Xu, Y Fujita, E Matsumoto Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 55 | 2022 |
| Clipped action policy gradient Y Fujita, S Maeda International Conference on Machine Learning, 1597-1606, 2018 | 52 | 2018 |
| Distributed reinforcement learning of targeted grasping with active vision for mobile manipulators Y Fujita, K Uenishi, A Ummadisingu, P Nagarajan, S Masuda, MY Castro 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2020 | 25 | 2020 |
| A differentiable gaussian-like distribution on hyperbolic space for gradient-based learning Y Nagano, S Yamaguchi, Y Fujita, M Koyama arXiv preprint arXiv:1902.02992, 41, 2019 | 16 | 2019 |
| Learning latent state spaces for planning through reward prediction A Havens, Y Ouyang, P Nagarajan, Y Fujita arXiv preprint arXiv:1912.04201, 2019 | 7 | 2019 |
| Toward onboard control system for mobile robots via deep reinforcement learning M Miyashita, S Maruyama, Y Fujita, M Kusumoto, T Pfeiffer, E Matsumoto, ... Deep RL Workshop at Neurips, 2018 | 3 | 2018 |
| Entropy Controllable Direct Preference Optimization M Omura, Y Fujita, T Kataoka arXiv preprint arXiv:2411.07595, 2024 | | 2024 |
| PLaMo-100B: A Ground-Up Language Model Designed for Japanese Proficiency K Abe, K Chubachi, Y Fujita, Y Hirokawa, K Imajo, T Kataoka, H Komatsu, ... arXiv preprint arXiv:2410.07563, 2024 | | 2024 |
