| Variational autoencoder with implicit optimal priors H Takahashi, T Iwata, Y Yamanaka, M Yamada, S Yagi Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 5066-5073, 2019 | 33 | 2019 |
| Autoencoding binary classifiers for supervised anomaly detection Y Yamanaka, T Iwata, H Takahashi, M Yamada, S Kanai Pacific Rim International Conference on Artificial Intelligence, 647-659, 2019 | 23 | 2019 |
| Omega-Omega interaction from 2+ 1-flavor lattice quantum chromodynamics M Yamada, K Sasaki, S Aoki, T Hatsuda, Y Ikeda, T Inoue, N Ishii, ... Progress of theoretical and experimental physics 2015 (7), 2015 | 22 | 2015 |
| Student-t Variational Autoencoder for Robust Density Estimation. H Takahashi, T Iwata, Y Yamanaka, M Yamada, S Yagi IJCAI, 2696-2702, 2018 | 21 | 2018 |
| SHELXS-97, Program for the Solution of Crystal Structures SHELXS-97, Program for the Solution of Crystal Structures, 1997 S Toyota, Y Okamoto, T Ishikawa, T IWANAGA, M YAMADA Bulletin of the Chemical Society of Japan 82 (10), 1287-1291, 2009 | 11 | 2009 |
| Macro action reinforcement learning with sequence disentanglement using variational autoencoder K Heecheol, M Yamada, K Miyoshi, H Yamakawa arXiv preprint arXiv:1903.09366, 2019 | 4 | 2019 |
| Favae: Sequence disentanglement using information bottleneck principle M Yamada, H Kim, K Miyoshi, H Yamakawa arXiv preprint arXiv:1902.08341, 2019 | 4 | 2019 |
| Disentangled representations for sequence data using information bottleneck principle M Yamada, H Kim, K Miyoshi, T Iwata, H Yamakawa Asian Conference on Machine Learning, 305-320, 2020 | 3 | 2020 |
| Molecular structure of chlorocycloheptane in inclusion compound with 9, 9′-bianthryl and gelation during crystallization S Toyota, Y Okamoto, T Ishikawa, T Iwanaga, M Yamada Bulletin of the Chemical Society of Japan 82 (2), 182-186, 2009 | 2 | 2009 |
| Detecting device, detecting method, and detecting program H Takahashi, T Iwata, Y Yamanaka, M Yamada, S Yagi US Patent App. 17/253,131, 2021 | 1 | 2021 |
| Constraining Logits by Bounded Function for Adversarial Robustness S Kanai, M Yamada, S Yamaguchi, H Takahashi, Y Ida 2021 International Joint Conference on Neural Networks (IJCNN), 1-8, 2021 | 1 | 2021 |
| Smoothness Analysis of Loss Functions of Adversarial Training S Kanai, M Yamada, H Takahashi, Y Yamanaka, Y Ida | 1 | 2021 |
| Absum: Simple regularization method for reducing structural sensitivity of convolutional neural networks S Kanai, Y Ida, Y Fujiwara, M Yamada, S Adachi Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 4394-4403, 2020 | 1 | 2020 |
| Reinforcement Learning in Latent Action Sequence Space H Kim, M Yamada, K Miyoshi, T Iwata, H Yamakawa 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2020 | 1 | 2020 |
| Familial cardiomyopathy with different clinical features in individual members K Kudo, M Yamada, H Imai, M Katagiri, S Ozawa, Y Ida, R Okada Journal of Cardiology 17 (4), 907-914, 1987 | 1 | 1987 |
| Evaluation device, evaluation method, and evaluation program T Takahashi, M Yamada US Patent App. 17/441,701, 2022 | | 2022 |
| Detection device and detection method M Yamada, Y Igarashi, Y Yamanaka US Patent App. 16/973,433, 2021 | | 2021 |
| Smoothness Analysis of Adversarial Training S Kanai, M Yamada, H Takahashi, Y Yamanaka, Y Ida arXiv preprint arXiv:2103.01400, 2021 | | 2021 |
| Adversarial Training Makes Weight Loss Landscape Sharper in Logistic Regression M Yamada, S Kanai, T Iwata, T Takahashi, Y Yamanaka, H Takahashi, ... arXiv preprint arXiv:2102.02950, 2021 | | 2021 |
| Student-t Variational Autoencoder for Robust Multivariate Density EstimationStudent-t VAE によるロバスト確率密度推定 H Takahashi, T Iwata, Y Yamanaka, M Yamada, S Yagi, H Kashima Transactions of the Japanese Society for Artificial Intelligence 36 (3), 2021 | | 2021 |
