| 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 | 69 | 2019 |
| Student-t Variational Autoencoder for Robust Density Estimation. H Takahashi, T Iwata, Y Yamanaka, M Yamada, S Yagi IJCAI, 2696-2702, 2018 | 43 | 2018 |
| Autoencoding binary classifiers for supervised anomaly detection Y Yamanaka, T Iwata, H Takahashi, M Yamada, S Kanai PRICAI 2019: Trends in Artificial Intelligence: 16th Pacific Rim …, 2019 | 38 | 2019 |
| Omega-Omega interaction from 2+ 1-flavor lattice quantum chromodynamics M Yamada, K Sasaki, S Aoki, T Doi, T Hatsuda, Y Ikeda, T Inoue, N Ishii, ... Progress of theoretical and experimental physics 2015 (7), 071B01, 2015 | 23 | 2015 |
| 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 | 16* | 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 | 8* | 2020 |
| Relationship between nonsmoothness in adversarial training, constraints of attacks, and flatness in the input space S Kanai, M Yamada, H Takahashi, Y Yamanaka, Y Ida IEEE Transactions on Neural Networks and Learning Systems, 2023 | 6 | 2023 |
| Smoothness analysis of adversarial training S Kanai, M Yamada, H Takahashi, Y Yamanaka, Y Ida arXiv preprint arXiv:2103.01400, 2021 | 6 | 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 | 5 | 2021 |
| The solutions for 3D-NAND processes with Canon's latest KrF scanner M Yamada, H Takeuchi, K Mishima, K Yoshimura, K Takahashi 2017 China Semiconductor Technology International Conference (CSTIC), 1-3, 2017 | 5 | 2017 |
| One-vs-the-rest loss to focus on important samples in adversarial training S Kanai, S Yamaguchi, M Yamada, H Takahashi, K Ohno, Y Ida International Conference on Machine Learning, 15669-15695, 2023 | 3 | 2023 |
| Learning optimal priors for task-invariant representations in variational autoencoders H Takahashi, T Iwata, A Kumagai, S Kanai, M Yamada, Y Yamanaka, ... Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and …, 2022 | 3 | 2022 |
| 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 | 3 | 2021 |
| BERT を用いたパケットペイロードの特徴抽出 山中友貴， 山田真徳， 高橋知克， 永井智大 人工知能学会全国大会論文集 第 35 回 (2021), 1F2GS10a04-1F2GS10a04, 2021 | 3 | 2021 |
| Detecting device, detecting method, and detecting program H Takahashi, T Iwata, Y Yamanaka, M Yamada, S Yagi US Patent App. 17/253,131, 2021 | 2 | 2021 |
| Smoothness analysis of loss functions of adversarial training S Kanai, M Yamada, H Takahashi, Y Yamanaka, Y Ida arXiv preprint arXiv:2103.01400, 2021 | 2 | 2021 |
| Dialogue System of Team NTT-EASE for DRC2023 Y Kubo, T Yamashita, M Yamada arXiv preprint arXiv:2312.13734, 2023 | 1 | 2023 |
| Revisiting permutation symmetry for merging models between different datasets M Yamada, T Yamashita, S Yamaguchi, D Chijiwa arXiv preprint arXiv:2306.05641, 2023 | 1 | 2023 |
| Detection device, detection method, and detection program T Takahashi, M Yamada, Y Yamanaka US Patent App. 17/794,984, 2023 | 1 | 2023 |
| Detection device and detection method M Yamada, Y Igarashi, Y Yamanaka US Patent 11,563,654, 2023 | 1 | 2023 |
Yuuki YamanakaNTT Secure Platform LaboratoriesVerified email at hco.ntt.co.jp
