2 papers
cs.LG2023
Structural Explanations for Graph Neural Networks using HSIC
Ayato Toyokuni, Makoto Yamada
Graph neural networks (GNNs) are a type of neural model that tackle graphical tasks in an end-to-end manner. Recently, GNNs have been receiving increased attention in machine learn…
cs.LG2021
Computationally Efficient Wasserstein Loss for Structured Labels
Ayato Toyokuni, Sho Yokoi, Hisashi Kashima +1
The problem of estimating the probability distribution of labels has been widely studied as a label distribution learning (LDL) problem, whose applications include age estimation,…