Zobrazeno 1 - 10
of 117
pro vyhledávání: '"Masayuki Hiromoto"'
Self-learning Monte Carlo (SLMC) methods are recently proposed to accelerate Markov chain Monte Carlo (MCMC) methods using a machine learning model. With latent generative models, SLMC methods realize efficient Monte Carlo updates with less autocorre
Externí odkaz:
http://arxiv.org/abs/2211.14024
Autor:
Hiromoto, Masayuki, Masayuki, Hiromoto
Publikováno v:
修道法学 = Shudo Law Review. 45(2):13-25
Publikováno v:
2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).
Publikováno v:
IEICE Transactions on Information and Systems. :396-405
Self-learning Monte Carlo (SLMC) methods are recently proposed to accelerate Markov chain Monte Carlo (MCMC) methods by using a machine learning model.With generative models having latent variables, SLMC methods realize efficient Monte Carlo updates
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cd3abe6c3b365cc865723fcf2862f11e
Publikováno v:
Integration. 69:335-344
In VLSI physical design, many algorithms require the solution of difficult combinatorial optimization problems such as max/min-cut, max-flow problems etc. Due to the vast number of elements typically found in this problem domain, these problems are c
Autor:
Masayuki, Hiromoto
Publikováno v:
修道法学 = Shudo Law Review. 42(1):1-8
Publikováno v:
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences. :430-439
Autor:
Takashi Hikihara, Kazuki Oishi, Michihiro Shintani, Masayuki Hiromoto, Yohei Nakamura, Takashi Sato
Publikováno v:
IEEE Transactions on Power Electronics. 33:10774-10783
Transistor models have been playing a key role in designing efficient power converters. As the operating frequency of the converters becomes higher, transistor models need to represent physical device behavior accurately. This paper proposes a compre
Autor:
Katsuya Hirota, Masayuki Hiromoto, Tamaki Yoshioka, Shuhei Hara, W. M. Snow, Tatsushi Shima, Ryota Kondo, Noriko Oi, Masaaki Kitaguchi, Kenji Mishima, Taichi Hori, Hirohiko M. Shimizu, Rintaro Nakabe, Christopher C. Haddock, Takashi Ino
Publikováno v:
Proceedings of the 3rd J-PARC Symposium (J-PARC2019).