no code implementations • 26 Apr 2022 • Hiori Kino, Hieu-Chi Dam, Takashi Miyake, Riichiro Mizoguchi
The proposed method was applied to materials informatics to demonstrate the systematic representation of expert knowledge and its usefullness.
no code implementations • 20 Aug 2020 • Duong-Nguyen Nguyen, Tien-Lam Pham, Viet-Cuong Nguyen, Hiori Kino, Takashi Miyake, Hieu-Chi Dam
We propose a data-driven method to extract dissimilarity between materials, with respect to a given target physical property.
no code implementations • 18 May 2020 • Phuoc Nguyen, Truyen Tran, Sunil Gupta, Santu Rana, Hieu-Chi Dam, Svetha Venkatesh
Given a target distribution, we predict the posterior distribution of the latent code, then use a matrix-network decoder to generate a posterior distribution q(\theta).
no code implementations • 23 Mar 2019 • Tran-Thai Dang, Tien-Lam Pham, Hiori Kino, Takashi Miyake, Hieu-Chi Dam
In this study, we establish a basis for selecting similarity measures when applying machine learning techniques to solve materials science problems.