10 citations · 15 across the 5 of their papers we have counts for
5 papers
Variational Bayes Decomposition for Inverse Estimation with Superimposed Multispectral Intensity
Akinori Asahara, Yoshihiro Osakabe, Yamamoto Mitsuya +1
A variational Bayesian inference for measured wave intensity, such as X-ray intensity, is proposed in this paper. The data is popular to obtain information about unobservable featu…
QUBO-inspired Molecular Fingerprint for Chemical Property Prediction
Koichiro Yawata, Yoshihiro Osakabe, Takuya Okuyama +1
Molecular fingerprints are widely used for predicting chemical properties, and selecting appropriate fingerprints is important. We generate new fingerprints based on the assumption…
QUBO Decision Tree: Annealing Machine Extends Decision Tree Splitting
Koichiro Yawata, Yoshihiro Osakabe, Takuya Okuyama +1
This paper proposes an extension of regression trees by quadratic unconstrained binary optimization (QUBO). Regression trees are very popular prediction models that are trainable w…
Proposing Novel Extrapolative Compounds by Nested Variational Autoencoders
Yoshihiro Osakabe, Akinori Asahara
Materials informatics (MI), which uses artificial intelligence and data analysis techniques to improve the efficiency of materials development, is attracting increasing interest fr…
Accelerating small-angle scattering experiments with simulation-based machine learning
Takuya Kanazawa, Akinori Asahara, Hidekazu Morita
Making material experiments more efficient is a high priority for materials scientists who seek to discover new materials with desirable properties. In this paper, we investigate h…