Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Waida, Hiroki"'
Autor:
Irobe, Hiroo, Aoki, Wataru, Yamazaki, Kimihiro, Zhang, Yuhui, Nakagawa, Takumi, Waida, Hiroki, Wada, Yuichiro, Kanamori, Takafumi
Advancing defensive mechanisms against adversarial attacks in generative models is a critical research topic in machine learning. Our study focuses on a specific type of generative models - Variational Auto-Encoders (VAEs). Contrary to common beliefs
Externí odkaz:
http://arxiv.org/abs/2407.18632
Self-supervised learning for image denoising problems in the presence of denaturation for noisy data is a crucial approach in machine learning. However, theoretical understanding of the performance of the approach that uses denatured data is lacking.
Externí odkaz:
http://arxiv.org/abs/2405.01124
Autor:
Nakagawa, Takumi, Sanada, Yutaro, Waida, Hiroki, Zhang, Yuhui, Wada, Yuichiro, Takanashi, Kōsaku, Yamada, Tomonori, Kanamori, Takafumi
Representation learning has been increasing its impact on the research and practice of machine learning, since it enables to learn representations that can apply to various downstream tasks efficiently. However, recent works pay little attention to t
Externí odkaz:
http://arxiv.org/abs/2304.09552
Autor:
Waida, Hiroki, Wada, Yuichiro, Andéol, Léo, Nakagawa, Takumi, Zhang, Yuhui, Kanamori, Takafumi
Contrastive learning is an efficient approach to self-supervised representation learning. Although recent studies have made progress in the theoretical understanding of contrastive learning, the investigation of how to characterize the clusters of th
Externí odkaz:
http://arxiv.org/abs/2304.00395
We consider the scenario of deep clustering, in which the available prior knowledge is limited. In this scenario, few existing state-of-the-art deep clustering methods can perform well for both non-complex topology and complex topology datasets. To a
Externí odkaz:
http://arxiv.org/abs/2303.03036
Autor:
Nakagawa, Takumi, Sanada, Yutaro, Waida, Hiroki, Zhang, Yuhui, Wada, Yuichiro, Takanashi, Kōsaku, Yamada, Tomonori, Kanamori, Takafumi
Publikováno v:
In Neural Networks January 2024 169:226-241
Autor:
Zhang Y; Tokyo Institute of Technology, Meguro-ku, Tokyo 152-8552, Japan zhang.y.av@m.titech.ac.jp., Wada Y; Fujitsu Limited, Nakahara-ku, Kawasaki, Kanagawa 211-8588, Japan.; RIKEN AIP, Chuo-ku, Tokyo 103-0027, Japan wada.yuichiro@jp.fujitsu.com., Waida H; Tokyo Institute of Technology, Meguro-ku, Tokyo 152-8552, Japan waida.h.aa@m.titech.ac.jp., Goto K; Tokyo Institute of Technology, Meguro-ku, Tokyo 152-8552, Japan kaitogoto7@gmail.com., Hino Y; Tokyo Institute of Technology, Meguro-ku, Tokyo 152-8552, Japan blueozk@gmail.com., Kanamori T; Tokyo Institute of Technology, Meguro-ku, Tokyo 152-8552, Japan.; RIKEN AIP, Chuo-ku, Tokyo 103-0027, Japan kanamori@c.titech.ac.jp.
Publikováno v:
Neural computation [Neural Comput] 2023 Jun 12; Vol. 35 (7), pp. 1288-1339.