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pro vyhledávání: '"Ando, Shin"'
Autor:
Ando, Shin
This paper proposes a deep representation learning using an information-theoretic loss with an aim to increase the inter-class distances as well as within-class similarity in the embedded space. Tasks such as anomaly and out-of-distribution detection
Externí odkaz:
http://arxiv.org/abs/2111.12950
Autor:
Yoshida, Masayoshi, Dajani, Hilmi R., Ando, Shin-ichi, Shimizu, Shuji, Bolic, Miodrag, Groza, Voicu
Publikováno v:
In Sleep Medicine January 2024 113:25-33
Akademický článek
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N-heterocyclic carbene ligands with a bicyclic framework fused with either naphthalene or anthracene
Publikováno v:
In Journal of Organometallic Chemistry 15 January 2023 984
Autor:
Ando, Shin
Class imbalance is a pervasive issue among classification models including deep learning, whose capacity to extract task-specific features is affected in imbalanced settings. However, the challenges of handling imbalance among a large number of class
Externí odkaz:
http://arxiv.org/abs/1810.12513
Autor:
Sawatari, Hiroyuki, Rahmawati, Anita, Moriyama, Nobuko, Fujita, Kanae, Ohkusa, Tomoko, Nao, Tomoko, Hashiguchi, Nobuko, Nishizaka, Mari, Ando, Shin-ichi, Chishaki, Akiko
Publikováno v:
In Sleep Medicine: X December 2022 4
Autor:
Faulx, Michael D., Mehra, Reena, Reis Geovanini, Glaucylara, Ando, Shin-ichi, Arzt, Michael, Drager, Luciano, Fu, Michael, Hoyos, Camilla, Hai, Jo, Hwang, Juey-Jen, Karaoguz, Remzi, Kimoff, John, Lee, Pei-Lin, Mediano, Olga, Patel, Sanjay R., Peker, Yüksel, Louis Pepin, Jean, Sanchez-de-la-Torre, Manuel, Sériès, Frédéric, Stadler, Stefan, Strollo, Patrick, Tahrani, A.A., Thunström, Erik, Yamauchi, Motoo, Redline, Susan, Phillips, Craig L.
Publikováno v:
In IJC Heart & Vasculature October 2022 42
Autor:
Ando, Shin1 (AUTHOR) ando@gpo.kumamoto-u.ac.jp, Tanaka, Ayako2 (AUTHOR), Ishizuka, Tadao1 (AUTHOR) tstone@gpo.kumamoto-u.ac.jp
Publikováno v:
European Journal of Organic Chemistry. 11/2/2023, Vol. 26 Issue 41, p1-6. 6p.
Autor:
Ando, Shin, Huang, Chun-Yuan
Class imbalance is a challenging issue in practical classification problems for deep learning models as well as traditional models. Traditionally successful countermeasures such as synthetic over-sampling have had limited success with complex, struct
Externí odkaz:
http://arxiv.org/abs/1704.07515
Publikováno v:
Sleep & Biological Rhythms; Oct2024, Vol. 22 Issue 4, p499-504, 6p