32 citations · 88 across the 7 of their papers we have counts for
7 papers
Data Transfer Approaches to Improve Seq-to-Seq Retrosynthesis
Katsuhiko Ishiguro, Kazuya Ujihara, Ryohto Sawada +2
Retrosynthesis is a problem to infer reactant compounds to synthesize a given product compound through chemical reactions. Recent studies on retrosynthesis focus on proposing more…
Learning Structured Latent Factors from Dependent Data:A Generative Model Framework from Information-Theoretic Perspective
Ruixiang Zhang, Masanori Koyama, Katsuhiko Ishiguro
Learning controllable and generalizable representation of multivariate data with desired structural properties remains a fundamental problem in machine learning. In this paper, we…
Weisfeiler-Lehman Embedding for Molecular Graph Neural Networks
Katsuhiko Ishiguro, Kenta Oono, Kohei Hayashi
A graph neural network (GNN) is a good choice for predicting the chemical properties of molecules. Compared with other deep networks, however, the current performance of a GNN is l…
Graph Residual Flow for Molecular Graph Generation
Shion Honda, Hirotaka Akita, Katsuhiko Ishiguro +2
Statistical generative models for molecular graphs attract attention from many researchers from the fields of bio- and chemo-informatics. Among these models, invertible flow-based…
Graph Warp Module: an Auxiliary Module for Boosting the Power of Graph Neural Networks in Molecular Graph Analysis
Katsuhiko Ishiguro, Shin-ichi Maeda, Masanori Koyama
Graph Neural Network (GNN) is a popular architecture for the analysis of chemical molecules, and it has numerous applications in material and medicinal science. Current lines of GN…
Machine-learning Selection of Optical Transients in Subaru/Hyper Suprime-Cam Survey
Mikio Morii, Shiro Ikeda, Nozomu Tominaga +8
We present an application of machine-learning (ML) techniques to source selection in the optical transient survey data with Hyper Suprime-Cam (HSC) on the Subaru telescope. Our goa…
Collapsed Variational Bayes Inference of Infinite Relational Model
Katsuhiko Ishiguro, Issei Sato, Naonori Ueda
The Infinite Relational Model (IRM) is a probabilistic model for relational data clustering that partitions objects into clusters based on observed relationships. This paper presen…