Zobrazeno 1 - 10
of 1 524
pro vyhledávání: '"P, Taurà"'
Autor:
Zheng, Lele, Cao, Yang, Jiang, Renhe, Taura, Kenjiro, Shen, Yulong, Li, Sheng, Yoshikawa, Masatoshi
Recent works show that sensitive user data can be reconstructed from gradient updates, breaking the key privacy promise of federated learning. While success was demonstrated primarily on image data, these methods do not directly transfer to other dom
Externí odkaz:
http://arxiv.org/abs/2410.16121
Autor:
Hanai, Masatoshi, Ishikawa, Ryo, Kawamura, Mitsuaki, Ohnishi, Masato, Takenaka, Norio, Nakamura, Kou, Matsumura, Daiju, Fujikawa, Seiji, Sakamoto, Hiroki, Ochiai, Yukinori, Okane, Tetsuo, Kuroki, Shin-Ichiro, Yamada, Atsuo, Suzumura, Toyotaro, Shiomi, Junichiro, Taura, Kenjiro, Mita, Yoshio, Shibata, Naoya, Ikuhara, Yuichi
In modern materials science, effective and high-volume data management across leading-edge experimental facilities and world-class supercomputers is indispensable for cutting-edge research. However, existing integrated systems that handle data from t
Externí odkaz:
http://arxiv.org/abs/2409.06734
Autor:
Zheng, Lele, Cao, Yang, Jiang, Renhe, Taura, Kenjiro, Shen, Yulong, Li, Sheng, Yoshikawa, Masatoshi
Spatiotemporal federated learning has recently raised intensive studies due to its ability to train valuable models with only shared gradients in various location-based services. On the other hand, recent studies have shown that shared gradients may
Externí odkaz:
http://arxiv.org/abs/2407.08529
Autor:
LLM-jp, Aizawa, Akiko, Aramaki, Eiji, Chen, Bowen, Cheng, Fei, Deguchi, Hiroyuki, Enomoto, Rintaro, Fujii, Kazuki, Fukumoto, Kensuke, Fukushima, Takuya, Han, Namgi, Harada, Yuto, Hashimoto, Chikara, Hiraoka, Tatsuya, Hisada, Shohei, Hosokawa, Sosuke, Jie, Lu, Kamata, Keisuke, Kanazawa, Teruhito, Kanezashi, Hiroki, Kataoka, Hiroshi, Katsumata, Satoru, Kawahara, Daisuke, Kawano, Seiya, Keyaki, Atsushi, Kiryu, Keisuke, Kiyomaru, Hirokazu, Kodama, Takashi, Kubo, Takahiro, Kuga, Yohei, Kumon, Ryoma, Kurita, Shuhei, Kurohashi, Sadao, Li, Conglong, Maekawa, Taiki, Matsuda, Hiroshi, Miyao, Yusuke, Mizuki, Kentaro, Mizuki, Sakae, Murawaki, Yugo, Nakamura, Ryo, Nakamura, Taishi, Nakayama, Kouta, Nakazato, Tomoka, Niitsuma, Takuro, Nishitoba, Jiro, Oda, Yusuke, Ogawa, Hayato, Okamoto, Takumi, Okazaki, Naoaki, Oseki, Yohei, Ozaki, Shintaro, Ryu, Koki, Rzepka, Rafal, Sakaguchi, Keisuke, Sasaki, Shota, Sekine, Satoshi, Suda, Kohei, Sugawara, Saku, Sugiura, Issa, Sugiyama, Hiroaki, Suzuki, Hisami, Suzuki, Jun, Suzumura, Toyotaro, Tachibana, Kensuke, Takagi, Yu, Takami, Kyosuke, Takeda, Koichi, Takeshita, Masashi, Tanaka, Masahiro, Taura, Kenjiro, Tolmachev, Arseny, Ueda, Nobuhiro, Wan, Zhen, Yada, Shuntaro, Yahata, Sakiko, Yamamoto, Yuya, Yamauchi, Yusuke, Yanaka, Hitomi, Yokota, Rio, Yoshino, Koichiro
This paper introduces LLM-jp, a cross-organizational project for the research and development of Japanese large language models (LLMs). LLM-jp aims to develop open-source and strong Japanese LLMs, and as of this writing, more than 1,500 participants
Externí odkaz:
http://arxiv.org/abs/2407.03963
Revealing and analyzing the various properties of materials is an essential and critical issue in the development of materials, including batteries, semiconductors, catalysts, and pharmaceuticals. Traditionally, these properties have been determined
Externí odkaz:
http://arxiv.org/abs/2308.08934
The prediction of material properties plays a crucial role in the development and discovery of materials in diverse applications, such as batteries, semiconductors, catalysts, and pharmaceuticals. Recently, there has been a growing interest in employ
Externí odkaz:
http://arxiv.org/abs/2308.08129
Autor:
Ille, Nicole, Nakao, Yoshiaki, Shumpei, Yano, Taura, Toshiyuki, Ebert, Arndt, Bornfleth, Harald, Asagi, Suguru, Kozawa, Kanoko, Itabashi, Izumi, Sato, Takafumi, Sakuraba, Rie, Tsuda, Rie, Kakisaka, Yosuke, Jin, Kazutaka, Nakasato, Nobukazu
Publikováno v:
Clinical Neurophysiology 158 (2024) 149-158
Objective: Analysis of the electroencephalogram (EEG) for epileptic spike and seizure detection or brain-computer interfaces can be severely hampered by the presence of artifacts. The aim of this study is to describe and evaluate a fast automatic alg
Externí odkaz:
http://arxiv.org/abs/2306.16910
Autor:
Suzumura, Toyotaro, Sugiki, Akiyoshi, Takizawa, Hiroyuki, Imakura, Akira, Nakamura, Hiroshi, Taura, Kenjiro, Kudoh, Tomohiro, Hanawa, Toshihiro, Sekiya, Yuji, Kobayashi, Hiroki, Matsushima, Shin, Kuga, Yohei, Nakamura, Ryo, Jiang, Renhe, Kawase, Junya, Hanai, Masatoshi, Miyazaki, Hiroshi, Ishizaki, Tsutomu, Shimotoku, Daisuke, Miyamoto, Daisuke, Aida, Kento, Takefusa, Atsuko, Kurimoto, Takashi, Sasayama, Koji, Kitagawa, Naoya, Fujiwara, Ikki, Tanimura, Yusuke, Aoki, Takayuki, Endo, Toshio, Ohshima, Satoshi, Fukazawa, Keiichiro, Date, Susumu, Uchibayashi, Toshihiro
The growing amount of data and advances in data science have created a need for a new kind of cloud platform that provides users with flexibility, strong security, and the ability to couple with supercomputers and edge devices through high-performanc
Externí odkaz:
http://arxiv.org/abs/2203.14188
This work proposes RaNNC (Rapid Neural Network Connector) as middleware for automatic hybrid parallelism. In recent deep learning research, as exemplified by T5 and GPT-3, the size of neural network models continues to grow. Since such models do not
Externí odkaz:
http://arxiv.org/abs/2103.16063
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