Zobrazeno 1 - 10
of 114 923
pro vyhledávání: '"Isao, A."'
Autor:
Oshima, Yugo, Ishii, Yasuyuki, Pratt, Francis L., Watanabe, Isao, Seo, Hitoshi, Tsumuraya, Takao, Miyazaki, Tsuyoshi, Kato, Reizo
The molecular triangular lattice system, beta'-EtMe3Sb[Pd(dmit)2]2, is considered as a candidate material for the quantum spin liquid (QSL) state, although ongoing debates arise from recent controversial results. Here, the results of electron spin re
Externí odkaz:
http://arxiv.org/abs/2410.20076
Autor:
Chang, Ching-Chun, Gao, Kai, Xu, Shuying, Kordoni, Anastasia, Leckie, Christopher, Echizen, Isao
Neural backdoors represent insidious cybersecurity loopholes that render learning machinery vulnerable to unauthorised manipulations, potentially enabling the weaponisation of artificial intelligence with catastrophic consequences. A backdoor attack
Externí odkaz:
http://arxiv.org/abs/2410.05284
The equilibrium selection problem in the generalized Nash equilibrium problem (GNEP) has recently been studied as an optimization problem, defined over the set of all variational equilibria achievable first through a non-cooperative game among player
Externí odkaz:
http://arxiv.org/abs/2409.11094
Autor:
Kume, Keita, Yamada, Isao
We propose a proximal variable smoothing algorithm for nonsmooth optimization problem with sum of three functions involving weakly convex composite function. The proposed algorithm is designed as a time-varying forward-backward splitting algorithm wi
Externí odkaz:
http://arxiv.org/abs/2409.10934
Autor:
Goto, Isao
This paper attempts to answer a "simple question" in building predictive models using machine learning algorithms. Although diagnostic and predictive models for various diseases have been proposed using data from large cohort studies and machine lear
Externí odkaz:
http://arxiv.org/abs/2409.01025
With the proliferation of AI agents in various domains, protecting the ownership of AI models has become crucial due to the significant investment in their development. Unauthorized use and illegal distribution of these models pose serious threats to
Externí odkaz:
http://arxiv.org/abs/2409.01541
Autor:
Minami, Shunya, Hayashi, Yoshihiro, Wu, Stephen, Fukumizu, Kenji, Sugisawa, Hiroki, Ishii, Masashi, Kuwajima, Isao, Shiratori, Kazuya, Yoshida, Ryo
To address the challenge of limited experimental materials data, extensive physical property databases are being developed based on high-throughput computational experiments, such as molecular dynamics simulations. Previous studies have shown that fi
Externí odkaz:
http://arxiv.org/abs/2408.04042
Autor:
Hisaki, Yukinari, Ono, Isao
In this paper, we propose an off-policy deep reinforcement learning (DRL) method utilizing the average reward criterion. While most existing DRL methods employ the discounted reward criterion, this can potentially lead to a discrepancy between the tr
Externí odkaz:
http://arxiv.org/abs/2408.01972
Autor:
Piotrowski, Tomasz, Yamada, Isao
Publikováno v:
Journal of the Franklin Institute, 2016
Reduced-rank approach has been used for decades in robust linear estimation of both deterministic and random vector of parameters in linear model y=Hx+\sqrt{epsilon}n. In practical settings, estimation is frequently performed under incomplete or inex
Externí odkaz:
http://arxiv.org/abs/2408.01117
Amid the proliferation of forged images, notably the tsunami of deepfake content, extensive research has been conducted on using artificial intelligence (AI) to identify forged content in the face of continuing advancements in counterfeiting technolo
Externí odkaz:
http://arxiv.org/abs/2407.18614