Prediction of Seismic Intensity Distributions Using Neural Networks. - Koyu Mizutani; Haruki Mitarai; Kakeru Miyazaki; Ryugo Shimamura; Soichiro Kumano; Toshihiko Yamasaki (GCCE, 2022)

Improving Robustness to out-of-Distribution Data by Frequency-Based Augmentation. - Koki Mukai; Soichiro Kumano; Toshihiko Yamasaki (ICIP, 2022)

Superclass Adversarial Attack. - Soichiro Kumano; Hiroshi Kera; Toshihiko Yamasaki (CoRR, 2022)

Adversarial Training from Mean Field Perspective. - Soichiro Kumano; Hiroshi Kera; Toshihiko Yamasaki (NeurIPS, 2023)

Sparse fooling images: Fooling machine perception through unrecognizable images. - Soichiro Kumano; Hiroshi Kera; Toshihiko Yamasaki (Pattern Recognit. Lett., 2023)

Adversarially Robust Continual Learning with Anti-Forgetting Loss. - Koki Mukai; Soichiro Kumano; Nicolas Michel; Ling Xiao 001; Toshihiko Yamasaki (ICIP, 2024)

Theoretical Understanding of Learning from Adversarial Perturbations. - Soichiro Kumano; Hiroshi Kera; Toshihiko Yamasaki (ICLR, 2024)

Wide Two-Layer Networks can Learn from Adversarial Perturbations. - Soichiro Kumano; Hiroshi Kera; Toshihiko Yamasaki (NeurIPS, 2024)

Data-Driven Prediction of Seismic Intensity Distributions Featuring Hybrid Classification-Regression Models. - Koyu Mizutani; Haruki Mitarai; Kakeru Miyazaki; Soichiro Kumano; Toshihiko Yamasaki (CoRR, 2024)

Language-guided Detection and Mitigation of Unknown Dataset Bias. - Zaiying Zhao; Soichiro Kumano; Toshihiko Yamasaki (CoRR, 2024)

Adversarially Pretrained Transformers may be Universally Robust In-Context Learners. - Soichiro Kumano; Hiroshi Kera; Toshihiko Yamasaki (CoRR, 2025)