Towards Understanding the Mechanism of Contrastive Learning via Similarity Structure: A Theoretical Analysis. - Hiroki Waida; Yuichiro Wada; Léo Andéol; Takumi Nakagawa; Yuhui Zhang; Takafumi Kanamori (ECML/PKDD (4), 2023)

Deep Clustering With a Constraint for Topological Invariance Based on Symmetric InfoNCE. - Yuhui Zhang; Yuichiro Wada; Hiroki Waida; Kaito Goto; Yusaku Hino; Takafumi Kanamori (Neural Comput., 2023)

Robust VAEs via Generating Process of Noise Augmented Data. - Hiroo Irobe; Wataru Aoki; Kimihiro Yamazaki; Yuhui Zhang; Takumi Nakagawa; Hiroki Waida; Yuichiro Wada; Takafumi Kanamori (ISIT, 2024)

Scaling-based Data Augmentation for Generative Models and its Theoretical Extension. - Yoshitaka Koike; Takumi Nakagawa; Hiroki Waida; Takafumi Kanamori (CoRR, 2024)

Denoising cosine similarity: A theory-driven approach for efficient representation learning. - Takumi Nakagawa; Yutaro Sanada; Hiroki Waida; Yuhui Zhang; Yuichiro Wada; Kosaku Takanashi; Tomonori Yamada; Takafumi Kanamori (Neural Networks, 2024)

Investigating self-supervised image denoising with denaturation. - Hiroki Waida; Kimihiro Yamazaki; Atsushi Tokuhisa; Mutsuyo Wada; Yuichiro Wada (Neural Networks, 2025)