Zobrazeno 1 - 10
of 2 100
pro vyhledávání: '"Nakamura Akira"'
Autor:
Kotegawa, Hisashi, Nakamura, Akira, Huyen, Vu Thi Ngoc, Arai, Yuki, Tou, Hideki, Sugawara, Hitoshi, Hayashi, Junichi, Takeda, Keiki, Tabata, Chihiro, Kaneko, Koji, Kodama, Katsuaki, Suzuki, Michi-To
Antiferromagnets without parity-time ($\mathcal{PT}$) symmetry offer novel perspectives in the field of functional magnetic materials. Among them, those with ferromagnetic-like responses are promising candidates for future applications such as antife
Externí odkaz:
http://arxiv.org/abs/2409.04064
Autor:
Nakayama, Shinji, Nakamura, Akira, Masaki, Yoshiharu, Ushio, Mako, Watanabe, Tomohiro, Tsujimae, Masahiro, Tanoue, Shiro, Maruo, Toru, Shiokawa, Masahiro, Yamane, Satoki, Kayashima, Atsuto, Takikawa, Tetsuya, Kikuta, Kazuhiro, Sano, Takanori, Ikeura, Tsukasa, Fujimori, Nao, Umemura, Takeji, Naitoh, Itaru, Nakase, Hiroshi, Isayama, Hiroyuki, Kanno, Atsushi, Kamata, Ken, Kodama, Yuzo, Inoue, Dai, Ido, Akio, Ueki, Toshiharu, Seno, Hiroshi, Yasuda, Hiroaki, Iwasaki, Eisuke, Nishino, Takayoshi, Kubota, Kensuke, Arizumi, Toshihiko, Tanaka, Atsushi, Uchida, Kazushige, Matsumoto, Ryotaro, Hamada, Shin, Nakamura, Seiji, Okazaki, Kazuichi, Takeyama, Yoshifumi, Masamune, Atsushi
Publikováno v:
In Pancreatology May 2024 24(3):335-342
Autor:
Narihira, Takuya, Alonsogarcia, Javier, Cardinaux, Fabien, Hayakawa, Akio, Ishii, Masato, Iwaki, Kazunori, Kemp, Thomas, Kobayashi, Yoshiyuki, Mauch, Lukas, Nakamura, Akira, Obuchi, Yukio, Shin, Andrew, Suzuki, Kenji, Tiedmann, Stephen, Uhlich, Stefan, Yashima, Takuya, Yoshiyama, Kazuki
While there exist a plethora of deep learning tools and frameworks, the fast-growing complexity of the field brings new demands and challenges, such as more flexible network design, speedy computation on distributed setting, and compatibility between
Externí odkaz:
http://arxiv.org/abs/2102.06725
Publikováno v:
Critical Readings on the Liberal Democratic Party in Japan Volume 4. :1412-1474
Autor:
Arashiro, Takeshi, Miwa, Maki, Nakagawa, Hidenori, Takamatsu, Junpei, Oba, Kunihiro, Fujimi, Satoshi, Kikuchi, Hitoshi, Iwasawa, Takamasa, Kanbe, Fumiko, Oyama, Keisuke, Kanai, Masayuki, Ogata, Yoshitaka, Asakura, Takanori, Asami, Takahiro, Mizuno, Keiko, Sugita, Manabu, Jinta, Torahiko, Nishida, Yusuke, Kato, Hideaki, Atagi, Kazuaki, Higaki, Taiki, Nakano, Yoshio, Tsutsumi, Takeya, Doi, Kent, Okugawa, Shu, Ueda, Akihiro, Nakamura, Akira, Yoshida, Toru, Shimada-Sammori, Kaoru, Shimizu, Keiki, Fujita, Yasuo, Okochi, Yasumi, Tochitani, Kentaro, Nakanishi, Asuka, Rinka, Hiroshi, Taniyama, Daisuke, Yamaguchi, Asase, Uchikura, Toshio, Matsunaga, Maiko, Aono, Hiromi, Hamaguchi, Masanari, Motoda, Kentaro, Nakayama, Sohei, Yamamoto, Kei, Oka, Hideaki, Tanaka, Katsushi, Inoue, Takeshi, Kobayashi, Mieko, Fujitani, Shigeki, Tsukahara, Maki, Takeda, Saki, Stucky, Ashley, Suzuki, Tadaki, Smith, Chris, Hibberd, Martin, Ariyoshi, Koya, Fujino, Yuji, Arima, Yuzo, Takeda, Shinhiro, Hashimoto, Satoru, Suzuki, Motoi
Publikováno v:
In Vaccine 25 January 2024 42(3):677-688
Akademický článek
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Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Arashiro, Takeshi, Arima, Yuzo, Kuramochi, Jin, Muraoka, Hirokazu, Sato, Akihiro, Chubachi, Kumi, Oba, Kunihiro, Yanai, Atsushi, Arioka, Hiroko, Uehara, Yuki, Ihara, Genei, Kato, Yasuyuki, Yanagisawa, Naoki, Nagura, Yoshito, Yanai, Hideki, Ueda, Akihiro, Numata, Akira, Kato, Hideaki, Oka, Hideaki, Nishida, Yusuke, Ishii, Koji, Ooki, Takao, Nidaira, Yuki, Asami, Takahiro, Jinta, Torahiko, Nakamura, Akira, Taniyama, Daisuke, Yamamoto, Kei, Tanaka, Katsushi, Ueshima, Kankuro, Fuwa, Tetsuji, Stucky, Ashley, Suzuki, Tadaki, Smith, Chris, Hibberd, Martin, Ariyoshi, Koya, Suzuki, Motoi
Publikováno v:
In Vaccine 13 November 2023 41(47):6969-6979
Autor:
Cardinaux, Fabien, Uhlich, Stefan, Yoshiyama, Kazuki, Garcia, Javier Alonso, Mauch, Lukas, Tiedemann, Stephen, Kemp, Thomas, Nakamura, Akira
Operating deep neural networks (DNNs) on devices with limited resources requires the reduction of their memory as well as computational footprint. Popular reduction methods are network quantization or pruning, which either reduce the word length of t
Externí odkaz:
http://arxiv.org/abs/1911.04951
Autor:
Uhlich, Stefan, Mauch, Lukas, Cardinaux, Fabien, Yoshiyama, Kazuki, Garcia, Javier Alonso, Tiedemann, Stephen, Kemp, Thomas, Nakamura, Akira
Efficient deep neural network (DNN) inference on mobile or embedded devices typically involves quantization of the network parameters and activations. In particular, mixed precision networks achieve better performance than networks with homogeneous b
Externí odkaz:
http://arxiv.org/abs/1905.11452