Preventing Gradient Explosions in Gated Recurrent Units. - Sekitoshi Kanai; Yasuhiro Fujiwara; Sotetsu Iwamura (NIPS, 2017)

Adaptive Data Pruning for Support Vector Machines. - Yasuhiro Fujiwara; Junya Arai; Sekitoshi Kanai; Yasutoshi Ida; Naonori Ueda (IEEE BigData, 2018)

Sigsoftmax: Reanalysis of the Softmax Bottleneck. - Sekitoshi Kanai; Yasuhiro Fujiwara; Yuki Yamanaka; Shuichi Adachi (NeurIPS, 2018)

Efficient Data Point Pruning for One-Class SVM. - Yasuhiro Fujiwara; Sekitoshi Kanai; Junya Arai; Yasutoshi Ida; Naonori Ueda (AAAI, 2019)

Fast Random Forest Algorithm via Incremental Upper Bound. - Yasuhiro Fujiwara; Yasutoshi Ida; Sekitoshi Kanai; Atsutoshi Kumagai; Junya Arai; Naonori Ueda (CIKM, 2019)

Autoencoding Binary Classifiers for Supervised Anomaly Detection. - Yuki Yamanaka; Tomoharu Iwata; Hiroshi Takahashi; Masanori Yamada; Sekitoshi Kanai (PRICAI (2), 2019)

Multiple Pretext-Task for Self-Supervised Learning via Mixing Multiple Image Transformations. - Shin'ya Yamaguchi; Sekitoshi Kanai; Tetsuya Shioda; Shoichiro Takeda (CoRR, 2019)

Absum: Simple Regularization Method for Reducing Structural Sensitivity of Convolutional Neural Networks. - Sekitoshi Kanai; Yasutoshi Ida; Yasuhiro Fujiwara; Masanori Yamada; Shuichi Adachi (AAAI, 2020)

Effective Data Augmentation with Multi-Domain Learning GANs. - Shin'ya Yamaguchi; Sekitoshi Kanai; Takeharu Eda (AAAI, 2020)

Fast Deterministic CUR Matrix Decomposition with Accuracy Assurance. - Yasutoshi Ida; Sekitoshi Kanai; Yasuhiro Fujiwara; Tomoharu Iwata; Koh Takeuchi 001; Hisashi Kashima (ICML, 2020)

Efficient Algorithm for the b-Matching Graph. - Yasuhiro Fujiwara; Atsutoshi Kumagai; Sekitoshi Kanai; Yasutoshi Ida; Naonori Ueda (KDD, 2020)

Fast and Accurate Anchor Graph-based Label Prediction. - Yasuhiro Fujiwara; Yasutoshi Ida; Atsutoshi Kumagai; Sekitoshi Kanai; Naonori Ueda (CIKM, 2021)

F-Drop&Match: GANs with a Dead Zone in the High-Frequency Domain. - Shin'ya Yamaguchi; Sekitoshi Kanai (ICCV, 2021)

Fast Similarity Computation for t-SNE. - Yasuhiro Fujiwara; Yasutoshi Ida; Sekitoshi Kanai; Atsutoshi Kumagai; Naonori Ueda (ICDE, 2021)

Image Enhanced Rotation Prediction for Self-Supervised Learning. - Shin'ya Yamaguchi; Sekitoshi Kanai; Tetsuya Shioda; Shoichiro Takeda (ICIP, 2021)

Constraining Logits by Bounded Function for Adversarial Robustness. - Sekitoshi Kanai; Masanori Yamada; Shin'ya Yamaguchi; Hiroshi Takahashi; Yasutoshi Ida (IJCNN, 2021)

Accelerate Inference of CNNs for Video Analysis While Preserving Exactness Exploiting Activation Sparsity. - Toshiaki Wakatsuki; Sekitoshi Kanai; Yasuhiro Fujiwara (MLSys, 2021)

Adversarial Training Makes Weight Loss Landscape Sharper in Logistic Regression. - Masanori Yamada; Sekitoshi Kanai; Tomoharu Iwata; Tomokatsu Takahashi; Yuki Yamanaka; Hiroshi Takahashi; Atsutoshi Kumagai (CoRR, 2021)

Smoothness Analysis of Loss Functions of Adversarial Training. - Sekitoshi Kanai; Masanori Yamada; Hiroshi Takahashi; Yuki Yamanaka; Yasutoshi Ida (CoRR, 2021)

Fast Algorithm for Anchor Graph Hashing. - Yasuhiro Fujiwara; Sekitoshi Kanai; Yasutoshi Ida; Atsutoshi Kumagai; Naonori Ueda (Proc. VLDB Endow., 2021)

Learning Optimal Priors for Task-Invariant Representations in Variational Autoencoders. - Hiroshi Takahashi; Tomoharu Iwata; Atsutoshi Kumagai; Sekitoshi Kanai; Masanori Yamada; Yuki Yamanaka; Hisashi Kashima (KDD, 2022)

Switching One-Versus-the-Rest Loss to Increase the Margin of Logits for Adversarial Robustness. - Sekitoshi Kanai; Shin'ya Yamaguchi; Masanori Yamada; Hiroshi Takahashi; Yasutoshi Ida (CoRR, 2022)

Fast Regularized Discrete Optimal Transport with Group-Sparse Regularizers. - Yasutoshi Ida; Sekitoshi Kanai; Kazuki Adachi; Atsutoshi Kumagai; Yasuhiro Fujiwara (AAAI, 2023)

Fast Saturating Gate for Learning Long Time Scales with Recurrent Neural Networks. - Kentaro Ohno; Sekitoshi Kanai; Yasutoshi Ida (AAAI, 2023)

Fast Block Coordinate Descent for Non-Convex Group Regularizations. - Yasutoshi Ida; Sekitoshi Kanai; Atsutoshi Kumagai (AISTATS, 2023)

Adversarial Finetuning with Latent Representation Constraint to Mitigate Accuracy-Robustness Tradeoff. - Satoshi Suzuki; Shin'ya Yamaguchi; Shoichiro Takeda; Sekitoshi Kanai; Naoki Makishima; Atsushi Ando; Ryo Masumura (ICCV, 2023)

One-vs-the-Rest Loss to Focus on Important Samples in Adversarial Training. - Sekitoshi Kanai; Shin'ya Yamaguchi; Masanori Yamada; Hiroshi Takahashi; Kentaro Ohno; Yasutoshi Ida (ICML, 2023)

Regularizing Neural Networks with Meta-Learning Generative Models. - Shin'ya Yamaguchi; Daiki Chijiwa; Sekitoshi Kanai; Atsutoshi Kumagai; Hisashi Kashima (NeurIPS, 2023)

Adaptive Random Feature Regularization on Fine-tuning Deep Neural Networks. - Shin'ya Yamaguchi; Sekitoshi Kanai; Kazuki Adachi; Daiki Chijiwa (CVPR, 2024)

Fast Iterative Hard Thresholding Methods with Pruning Gradient Computations. - Yasutoshi Ida; Sekitoshi Kanai; Atsutoshi Kumagai; Tomoharu Iwata; Yasuhiro Fujiwara (NeurIPS, 2024)

Evaluating Time-Series Training Dataset through Lens of Spectrum in Deep State Space Models. - Sekitoshi Kanai; Yasutoshi Ida; Kazuki Adachi; Mihiro Uchida; Tsukasa Yoshida; Shin'ya Yamaguchi (CoRR, 2024)

Relationship Between Nonsmoothness in Adversarial Training, Constraints of Attacks, and Flatness in the Input Space. - Sekitoshi Kanai; Masanori Yamada; Hiroshi Takahashi; Yuki Yamanaka; Yasutoshi Ida (IEEE Trans. Neural Networks Learn. Syst., 2024)

Post-pre-training for Modality Alignment in Vision-Language Foundation Models. - Shin'ya Yamaguchi; Dewei Feng; Sekitoshi Kanai; Kazuki Adachi; Daiki Chijiwa (CVPR, 2025)

Transfer learning with pre-trained conditional generative models. - Shin'ya Yamaguchi; Sekitoshi Kanai; Atsutoshi Kumagai; Daiki Chijiwa; Hisashi Kashima (Mach. Learn., 2025)