He received his B.E. and M.E. degrees in Physics and Physico-Informatics Engineering from Keio University, Japan, in 2013, 2015, respectively. He recieved the Ph.D. degree in Engineering from Keio University, Japan in 2020. He is a researcher of NTT since 2015.

Publications

Journal

  1. Sekitoshi Kanai, Masanori Yamada, Hiroshi Takahashi, Yuki Yamanaka, Yasutoshi Ida,
    Relationship between Non-smoothness in Adversarial Training, Constraints of Attacks, and Flatness in the Input Space,
    IEEE TNNLS (to appear)
    [Early Access], [preprint (arXiv)]
  2. Yasuhiro Fujiwara, Sekitoshi Kanai, Yasutoshi Ida, Atsutoshi Kumagai, Naonori Ueda,
    Fast Algorithm for Anchor Graph Hashing, PVLDB2021, Vol. 14, No. 6, pp.916-928, (2021)
    [paper]
  3. Sekitoshi Kanai, Maho Sugaya, Shuichi Adachi, Kentaro Matsui,
    Low-complexity simultaneous estimation of head-related transfer functions by prediction error method,
    Journal of the Audio Engineering Society, Vol.64, No.11, pp.895-904, (2016)
    [paper]

International Conference

  1. Shin’ya Yamaguchi, Sekitoshi Kanai, Kazuki Adachi, Daiki Chijiwa,
    Adaptive Random Feature Regularization on Fine-tuning Deep Neural Networks, CVPR 2023 (To appear)
    [arXiv],
  2. Shin’ya Yamaguchi, Daiki Chijiwa, Sekitoshi Kanai, Atsutoshi Kumagai, Hisashi Kashima
    Regularizing Neural Networks with Meta-Learning Generative Models, NeurIPS 2023
    [arXiv],[paper]
  3. Satoshi Suzuki, Shin’ya Yamaguchi, Shoichiro Takeda, Sekitoshi Kanai, Naoki Makishima, Atsushi Ando, Ryo Masumura
    Adversarial Finetuning with Latent Representation Constraint to Mitigate Accuracy-Robustness Tradeoff, ICCV 2023
    [paper], [arXiv]
  4. Sekitoshi Kanai, Shin’ya Yamaguchi, Masanori Yamada, Hiroshi Takahashi, Kentaro Ohno, Yasutoshi Ida,
    One-vs-the-Rest Loss to Focus on Important Samples in Adversarial Training, ICML 2023
    [paper], [video & poster], [arXiv]
  5. Yasutoshi Ida, Sekitoshi Kanai, Atsutoshi Kumagai
    Fast Block Coordinate Descent for Non-Convex Group Regularizations, AISTATS 2023
    [paper]
  6. Kentaro Ohno, Sekitoshi Kanai, Yasutoshi Ida,
    Fast Saturating Gate for Learning Long Time Scales with Recurrent Neural Networks, AAAI 2023
    [paper], [arXiv]
  7. Yasutoshi Ida, Sekitoshi Kanai, Kazuki Adachi, Atsutoshi Kumagai, Yasuhiro Fujiwara,
    Fast Regularized Discrete Optimal Transport with Group-sparse Regularizers, AAAI 2023
    [paper], [arXiv]
  8. Hiroshi Takahashi, Tomoharu Iwata, Atsutoshi Kumagai, Sekitoshi Kanai, Masanori Yamada, Yuuki Yamanaka, and Hisashi Kashima,
    Learning Optimal Priors for Task-Invariant Representations in Variational Autoencoders, KDD2022
    [paper]
  9. Yasuhiro Fujiwara, Yasutoshi Ida, Atsutoshi Kumagai, Sekitoshi Kanai, and Naonori Ueda,
    Fast and Accurate Anchor Graph-based Label Prediction, CIKM2021
    [paper]
  10. Shin’ya Yamaguchi, Sekitoshi Kanai,
    F-Drop&Match: GANs with a Dead Zone in the High-Frequency Domain, ICCV 2021
    [paper], [arXiv]
  11. Shin’ya Yamaguchi, Sekitoshi Kanai, Tetsuya Shioda, Shoichiro Takeda,
    Multiple Pretext-Task for Self-Supervised Learning via Mixing Multiple Image Transformations, ICIP2021
    [paper], [arxiv]
  12. Sekitoshi Kanai, Masanori Yamada, Shin’ya Yamaguchi, Hiroshi Takahashi, Yasutoshi Ida,
    Constraining Logits by Bounded Function for Adversarial Robustness, IJCNN2021
    [paper], [arxiv]
  13. Toshiaki Wakatsuki, Sekitoshi Kanai, Yasuhiro Fujiwara,
    Accelerate Inference of CNNs for Video Analysis While Preserving Exactness Exploiting Activation Sparsity, MLSys2021
    [paper]
  14. Yasuhiro Fujiwara, Yasutoshi Ida, Sekitoshi Kanai, Atsutoshi Kumagai, Naonori Ueda,
    Fast Similarity Computation for t-SNE, ICDE2021
    [paper]
  15. Yasuhiro Fujiwara, Atsutoshi Kumagai, Sekitoshi Kanai, Yasutoshi Ida, and Naonori Ueda,
    Efficient Algorithm for the b-Matching, KDD2020
    [paper]
  16. Yasutoshi Ida, Sekitoshi Kanai,Yasuhiro Fujiwara, Tomoharu Iwata, Koh Takeuchi, and Hisashi Kashima,
    Fast Deterministic CUR Matrix Decomposition with Accuracy Assurance, ICML2020,
    [paper]
  17. Sekitoshi Kanai, Yasutoshi Ida, Yasuhiro Fujiwara, Masanori Yamada, Shuichi Adachi,
    Absum: Simple Regularization Method for Reducing Structural Sensitivity of Convolutional Neural Networks, AAAI2020
    [paper], [arxiv]
  18. Shin’ya Yamaguchi, Sekitoshi Kanai, Takeharu Eda,
    Effective Data Augmentation with Multi-Domain Learning GANs, AAAI 2020
    [paper], [arxiv]
  19. Yasuhiro Fujiwara, Yasutoshi Ida, Sekitoshi Kanai, Atsutoshi Kumagai, Junya Arai, Naonori Ueda,
    Fast Random Forest Algorithm via Incremental Upper Bound, CIKM 2019
    [paper]
  20. Yuki Yamanaka, Tomoharu Iwata, Hiroshi Takahashi, Masanori Yamada, Sekitoshi Kanai,
    Autoencoding Binary Classifiers for Supervised Anomaly Detection, PRICAI2019
    [paper]
  21. Yasuhiro Fujiwara, Sekitoshi Kanai, Junya Arai, Yasutoshi Ida, Naonori Ueda,
    Efficient Data Point Pruning for One-Class SVM, AAAI2019
    [paper]
  22. Yasuhiro Fujiwara, Junya Arai, Sekitoshi Kanai, Yasutoshi Ida, Naonori Ueda,
    Adaptive Data Pruning for Support Vector Machines, 2018 IEEE International Conference on Big Data
    [paper]
  23. Sekitoshi Kanai, Yasuhiro Fujiwara, Yuki Yamanaka, Shuichi Adachi,
    Sigsoftmax: Reanalysis of the Softmax Bottleneck, NeurIPS 2018
    [paper], [arxiv]
  24. Sekitoshi Kanai, Yasuhiro Fujiwara, Sotetsu Iwamura,
    Preventing Gradient Explosions in Gated Recurrent Units, NIPS 2017
    [paper]
  25. Sekitoshi Kanai, Kentaro Matsui, Yasushige Nakayama, Shuichi Adachi,
    Uncorrelated Input Signals Design and Identification with Low-Complexity for Simultaneous Estimation of Head-Related Transfer Functions, 137th Audio Engineering Society Convention (2014)
    [paper]
  26. Sekitoshi Kanai, Kentaro Matsui, Shuichi Adachi,
    Identification input design for simultaneous estimation of head-related transfer functions, SICE2014
    [paper]

Preprints

  1. Shin’ya Yamaguchi, Sekitoshi Kanai, Atsutoshi Kumagai, Daiki Chijiwa, Hisashi Kashima
    Transfer Learning with Pre-trained Conditional Generative Models
    arXiv:2204.12833, 2022.
    [arXiv]
  2. Masanori Yamada, Sekitoshi Kanai, Tomoharu Iwata, Tomokatsu Takahashi, Yuki Yamanaka, Hiroshi Takahashi, Atsutoshi Kumagai,
    Adversarial Training Makes Weight Loss Landscape Sharper in Logistic Regression,
    arXiv:2102.02950, 2021.
    [arXiv]

Activities

Reviewer:

業績（日本語）