Theoretical Analysis of the Advantage of Deepening Neural Networks. - Yasushi Esaki; Yuta Nakahara; Toshiyasu Matsushima (ICMLA, 2020)

The Ratio of the Desired Parameters of Deep Neural Networks. - Yasushi Esaki; Yuta Nakahara; Toshiyasu Matsushima (IEICE Trans. Fundam. Electron. Commun. Comput. Sci., 2022)

StyleDiff: Attribute comparison between unlabeled datasets in latent disentangled space. - Keisuke Kawano; Takuro Kutsuna; Ryoko Tokuhisa; Akihiro Nakamura; Yasushi Esaki (Image Vis. Comput., 2023)

Accuracy-Preserving Calibration via Statistical Modeling on Probability Simplex. - Yasushi Esaki; Akihiro Nakamura; Keisuke Kawano; Ryoko Tokuhisa; Takuro Kutsuna (AISTATS, 2024)

One-Shot Domain Incremental Learning. - Yasushi Esaki; Satoshi Koide; Takuro Kutsuna (IJCNN, 2024)

CT-OT Flow: Estimating Continuous-Time Dynamics from Discrete Temporal Snapshots. - Keisuke Kawano; Takuro Kutsuna; Naoki Hayashi; Yasushi Esaki; Hidenori Tanaka (CoRR, 2025)