150 citations · 331 across the 15 of their papers we have counts for
7 papers · 1 filter
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…
Einconv: Exploring Unexplored Tensor Network Decompositions for Convolutional Neural Networks
Kohei Hayashi, Taiki Yamaguchi, Yohei Sugawara +1
Tensor decomposition methods are widely used for model compression and fast inference in convolutional neural networks (CNNs). Although many decompositions are conceivable, only CP…
Data Interpolating Prediction: Alternative Interpretation of Mixup
Takuya Shimada, Shoichiro Yamaguchi, Kohei Hayashi +1
Data augmentation by mixing samples, such as Mixup, has widely been used typically for classification tasks. However, this strategy is not always effective due to the gap between a…
Minimizing Quadratic Functions in Constant Time
Kohei Hayashi, Yuichi Yoshida
A sampling-based optimization method for quadratic functions is proposed. Our method approximately solves the following -dimensional quadratic minimization problem in constant t…
A Tractable Fully Bayesian Method for the Stochastic Block Model
Kohei Hayashi, Takuya Konishi, Tatsuro Kawamoto
The stochastic block model (SBM) is a generative model revealing macroscopic structures in graphs. Bayesian methods are used for (i) cluster assignment inference and (ii) model sel…
Rebuilding Factorized Information Criterion: Asymptotically Accurate Marginal Likelihood
Kohei Hayashi, Shin-ichi Maeda, Ryohei Fujimaki
Factorized information criterion (FIC) is a recently developed approximation technique for the marginal log-likelihood, which provides an automatic model selection framework for a…
Factorized Asymptotic Bayesian Hidden Markov Models
Ryohei Fujimaki, Kohei Hayashi
This paper addresses the issue of model selection for hidden Markov models (HMMs). We generalize factorized asymptotic Bayesian inference (FAB), which has been recently developed f…