Zobrazeno 1 - 10
of 66 919
pro vyhledávání: '"Kawano A"'
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
Rapport is known as a conversational aspect focusing on relationship building, which influences outcomes in collaborative tasks. This study aims to establish human-agent rapport through small talk by using a rapport-building strategy. We implemented
Externí odkaz:
http://arxiv.org/abs/2406.09839
Area-level models for small area estimation typically rely on areal random effects to shrink design-based direct estimates towards a model-based predictor. Incorporating the spatial dependence of the random effects into these models can further impro
Externí odkaz:
http://arxiv.org/abs/2404.12463
Autor:
Okazaki, Akira, Kawano, Shuichi
Multi-task learning (MTL) aims to improve estimation and prediction performance by sharing common information among related tasks. One natural assumption in MTL is that tasks are classified into clusters based on their characteristics. However, exist
Externí odkaz:
http://arxiv.org/abs/2404.03250
Autor:
Ueda, Nobuhiro, Habe, Hideko, Matsui, Yoko, Yuguchi, Akishige, Kawano, Seiya, Kawanishi, Yasutomo, Kurohashi, Sadao, Yoshino, Koichiro
Understanding expressions that refer to the physical world is crucial for such human-assisting systems in the real world, as robots that must perform actions that are expected by users. In real-world reference resolution, a system must ground the ver
Externí odkaz:
http://arxiv.org/abs/2403.19259
Situated conversations, which refer to visual information as visual question answering (VQA), often contain ambiguities caused by reliance on directive information. This problem is exacerbated because some languages, such as Japanese, often omit subj
Externí odkaz:
http://arxiv.org/abs/2403.17545
Autor:
Kawano, Masataka
Quasiparticle excitations in material solids often experience a fictitious gauge field, which can be a potential source of intriguing transport phenomena. Here, we show that low-energy excitations in insulating antiferromagnetic skyrmion crystals on
Externí odkaz:
http://arxiv.org/abs/2403.11655
Autor:
Kawano, Yasufumi, Aoki, Yoshimitsu
Semantic segmentation is a crucial task in computer vision, where each pixel in an image is classified into a category. However, traditional methods face significant challenges, including the need for pixel-level annotations and extensive training. F
Externí odkaz:
http://arxiv.org/abs/2403.11197
Autor:
Kawano, Yasufumi, Aoki, Yoshimitsu
Semantic segmentation is essential in computer vision for various applications, yet traditional approaches face significant challenges, including the high cost of annotation and extensive training for supervised learning. Additionally, due to the lim
Externí odkaz:
http://arxiv.org/abs/2403.11194
In this paper, we examine the role of stochastic quantizers for privacy preservation. We first employ a static stochastic quantizer and investigate its corresponding privacy-preserving properties. Specifically, we demonstrate that a sufficiently larg
Externí odkaz:
http://arxiv.org/abs/2403.03048