Zobrazeno 1 - 10
of 74
pro vyhledávání: '"Otsuka, Takuma"'
Autor:
Wakabayashi, Yuki K., Otsuka, Takuma, Krockenberger, Yoshiharu, Sawada, Hiroshi, Taniyasu, Yoshitaka, Yamamoto, Hideki
Perovskite insulator SrTiO3 is expected to be applied to the next generation of electronic and photonic devices as high-k capacitors and photocatalysts. However, reproducible growth of highly insulating stoichiometric SrTiO3 films remains challenging
Externí odkaz:
http://arxiv.org/abs/2303.00929
Autor:
Wakabayashi, Yuki K., Otsuka, Takuma, Krockenberger, Yoshiharu, Sawada, Hiroshi, Taniyasu, Yoshitaka, Yamamoto, Hideki
A crucial problem in achieving innovative high-throughput materials growth with machine learning and automation techniques, such as Bayesian optimization (BO) and robotic experimentation, has been a lack of an appropriate way to handle missing data d
Externí odkaz:
http://arxiv.org/abs/2204.05452
Autor:
Takiguchi, Kosuke, Wakabayashi, Yuki K., Irie, Hiroshi, Krockenberger, Yoshiharu, Otsuka, Takuma, Sawada, Hiroshi, Nikolaev, Sergey A., Das, Hena, Tanaka, Masaaki, Taniyasu, Yoshitaka, Yamamoto, Hideki
Magnetic Weyl fermions, which occur in magnets, have novel transport phenomena related to pairs of Weyl nodes, and they are, of both, scientific and technological interest, with the potential for use in high-performance electronics, spintronics and q
Externí odkaz:
http://arxiv.org/abs/2004.00810
Autor:
Iwata, Tomoharu, Otsuka, Takuma
We propose an efficient transfer Bayesian optimization method, which finds the maximum of an expensive-to-evaluate black-box function by using data on related optimization tasks. Our method uses auxiliary information that represents the task characte
Externí odkaz:
http://arxiv.org/abs/1909.07670
Autor:
Wakabayashi, Yuki K., Otsuka, Takuma, Krockenberger, Yoshiharu, Sawada, Hiroshi, Taniyasu, Yoshitaka, Yamamoto, Hideki
Materials informatics exploiting machine learning techniques, e.g., Bayesian optimization (BO), has the potential to offer high-throughput optimization of thin-film growth conditions through incremental updates of machine learning models in accordanc
Externí odkaz:
http://arxiv.org/abs/1908.00739
Stochastic gradient Langevin dynamics (SGLD) is a computationally efficient sampler for Bayesian posterior inference given a large scale dataset. Although SGLD is designed for unbounded random variables, many practical models incorporate variables wi
Externí odkaz:
http://arxiv.org/abs/1903.02750
Autor:
Iwata, Tomoharu, Otsuka, Takuma, Shimizu, Hitoshi, Sawada, Hiroshi, Naya, Futoshi, Ueda, Naonori
Appropriate traffic regulations, e.g. planned road closure, are important in congested events. Crowd simulators have been used to find appropriate regulations by simulating multiple scenarios with different regulations. However, this approach require
Externí odkaz:
http://arxiv.org/abs/1810.09712
Factorization machines and polynomial networks are supervised polynomial models based on an efficient low-rank decomposition. We extend these models to the multi-output setting, i.e., for learning vector-valued functions, with application to multi-cl
Externí odkaz:
http://arxiv.org/abs/1705.07603
Autor:
Otsuka, Takuma
0048
甲第18412号
情博第527号
新制||情||93(附属図書館)
31270
(主査)教授 奥乃 博, 教授 河原 達也, 准教授 CUTURI CAMETO Marco, 講師 吉井 和佳
学位規則第4条第1項該当
Doctor
甲第18412号
情博第527号
新制||情||93(附属図書館)
31270
(主査)教授 奥乃 博, 教授 河原 達也, 准教授 CUTURI CAMETO Marco, 講師 吉井 和佳
学位規則第4条第1項該当
Doctor
Externí odkaz:
http://hdl.handle.net/2433/188871
Autor:
Wakabayashi, Yuki K.1 yuuki.wakabayashi.we@hco.ntt.co.jp, Otsuka, Takuma2 yuuki.wakabayashi.we@hco.ntt.co.jp, Krockenberger, Yoshiharu1, Sawada, Hiroshi2, Taniyasu, Yoshitaka1, Yamamoto, Hideki1
Publikováno v:
APL Machine Learning. Jun2023, Vol. 1 Issue 2, p1-14. 14p.
