Zobrazeno 1 - 10
of 361
pro vyhledávání: '"KAWASAKI, EIJI"'
Computing atomic-scale properties of chemically disordered materials requires an efficient exploration of their vast configuration space. Traditional approaches such as Monte Carlo or Special Quasirandom Structures either entail sampling an excessive
Externí odkaz:
http://arxiv.org/abs/2408.14928
Learning a parametric model from a given dataset indeed enables to capture intrinsic dependencies between random variables via a parametric conditional probability distribution and in turn predict the value of a label variable given observed variable
Externí odkaz:
http://arxiv.org/abs/2406.09172
We present new Bayesian Last Layer models in the setting of multivariate regression under heteroscedastic noise, and propose an optimization algorithm for parameter learning. Bayesian Last Layer combines Bayesian modelling of the predictive distribut
Externí odkaz:
http://arxiv.org/abs/2405.01761
Autor:
Dalgaty, Thomas, Yamada, Shogo, Molnos, Anca, Kawasaki, Eiji, Mesquida, Thomas, Rummens, François, Shibata, Tatsuo, Urakawa, Yukihiro, Terasaki, Yukio, Sasaki, Tomoyuki, Duranton, Marc
By exploiting the intrinsic random nature of nanoscale devices, Memristor Monte Carlo (MMC) is a promising enabler of edge learning systems. However, due to multiple algorithmic and device-level limitations, existing demonstrations have been restrict
Externí odkaz:
http://arxiv.org/abs/2312.02771
Autor:
Karcz, Maciej J., Messina, Luca, Kawasaki, Eiji, Rajaonson, Serenah, Bathellier, Didier, Bourasseau, Emeric
Machine-learning methods are nowadays of common use in the field of material science. For example, they can aid in optimizing the physicochemical properties of new materials, or help in the characterization of highly complex chemical compounds. An es
Externí odkaz:
http://arxiv.org/abs/2211.12086
Markov Chain Monte Carlo (MCMC) algorithms do not scale well for large datasets leading to difficulties in Neural Network posterior sampling. In this paper, we propose Penalty Bayesian Neural Networks - PBNNs, as a new algorithm that allows the evalu
Externí odkaz:
http://arxiv.org/abs/2210.09141
In this paper we propose Discretely Indexed flows (DIF) as a new tool for solving variational estimation problems. Roughly speaking, DIF are built as an extension of Normalizing Flows (NF), in which the deterministic transport becomes stochastic, and
Externí odkaz:
http://arxiv.org/abs/2204.01361
Autor:
Kawasaki, Eiji1 (AUTHOR) e-kawasaki@tenjinkai.or.jp, Jinnouchi, Hideaki2 (AUTHOR) hideaki@jinnouchi.or.jp, Maeda, Yasutaka3 (AUTHOR) myas555@minami-cl.jp, Okada, Akira4 (AUTHOR) okada@okadaclinic.or.jp, Kawai, Koichi5 (AUTHOR) info@kawai-clinic.com
Publikováno v:
International Journal of Molecular Sciences. Jul2024, Vol. 25 Issue 14, p7618. 11p.
Autor:
Kawasaki, Eiji1 (AUTHOR) e-kawasaki@tenjinkai.or.jp, Awata, Takuya2 (AUTHOR), Ikegami, Hiroshi3 (AUTHOR), Imagawa, Akihisa4 (AUTHOR), Oikawa, Yoichi5 (AUTHOR), Osawa, Haruhiko6 (AUTHOR), Katsuki, Takeshi7 (AUTHOR), Kanatsuna, Norio4 (AUTHOR), Kawamura, Ryoichi6 (AUTHOR), Kozawa, Junji8 (AUTHOR), Kodani, Noriko9 (AUTHOR), Kobayashi, Tetsuro10 (AUTHOR), Shimada, Akira5 (AUTHOR), Shimoda, Masayuki2 (AUTHOR), Takahashi, Kazuma11 (AUTHOR), Chujo, Daisuke12 (AUTHOR), Tsujimoto, Tetsuro13 (AUTHOR), Tsuchiya, Kyoichiro14 (AUTHOR), Terakawa, Aiko9 (AUTHOR), Terasaki, Jungo4 (AUTHOR)
Publikováno v:
Journal of Diabetes Investigation. Jul2024, Vol. 15 Issue 7, p835-842. 8p.
Autor:
Shimada, Akira1 (AUTHOR) asmd@saitama-med.ac.jp, Kawasaki, Eiji2 (AUTHOR), Abiru, Norio3 (AUTHOR), Awata, Takuya4 (AUTHOR), Oikawa, Yoichi1 (AUTHOR), Osawa, Haruhiko5 (AUTHOR), Kajio, Hiroshi6 (AUTHOR), Kozawa, Junji7 (AUTHOR), Takahashi, Kazuma8 (AUTHOR), Chujo, Daisuke9 (AUTHOR), Noso, Shinsuke10 (AUTHOR), Fukui, Tomoyasu11 (AUTHOR), Miura, Junnosuke12 (AUTHOR), Yasuda, Kazuki13 (AUTHOR), Yasuda, Hisafumi14 (AUTHOR), Imagawa, Akihisa15 (AUTHOR), Ikegami, Hiroshi10 (AUTHOR)
Publikováno v:
Journal of Diabetes Investigation. Feb2024, Vol. 15 Issue 2, p254-257. 4p.