185 citations · 419 across the 15 of their papers we have counts for
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cs.LG2019★ 31 cited
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…
cs.LG2019★ 185 cited
Graph Neural Networks Exponentially Lose Expressive Power for Node Classification
Kenta Oono, Taiji Suzuki
Graph Neural Networks (graph NNs) are a promising deep learning approach for analyzing graph-structured data. However, it is known that they do not improve (or sometimes worsen) th…
stat.ML2019★ 28 cited
Approximation and Non-parametric Estimation of ResNet-type Convolutional Neural Networks
Kenta Oono, Taiji Suzuki
Convolutional neural networks (CNNs) have been shown to achieve optimal approximation and estimation error rates (in minimax sense) in several function classes. However, previous a…