Ryoma Sato
Ryoma Sato
Verified email at ml.ist.i.kyoto-u.ac.jp - Homepage
Title
Cited by
Cited by
Year
Approximation ratios of graph neural networks for combinatorial problems
R Sato, M Yamada, H Kashima
arXiv preprint arXiv:1905.10261, 2019
232019
A survey on the expressive power of graph neural networks
R Sato
arXiv preprint arXiv:2003.04078, 2020
192020
Random features strengthen graph neural networks
R Sato, M Yamada, H Kashima
arXiv preprint arXiv:2002.03155, 2020
122020
Short-term precipitation prediction with skip-connected prednet
R Sato, H Kashima, T Yamamoto
International Conference on Artificial Neural Networks, 373-382, 2018
82018
Fast unbalanced optimal transport on tree
R Sato, M Yamada, H Kashima
arXiv preprint arXiv:2006.02703, 2020
42020
Constant time graph neural networks
R Sato, M Yamada, H Kashima
arXiv preprint arXiv:1901.07868, 2019
32019
Fast and Robust Comparison of Probability Measures in Heterogeneous Spaces
R Sato, M Cuturi, M Yamada, H Kashima
arXiv preprint arXiv:2002.01615, 2020
22020
Poincare: Recommending Publication Venues via Treatment Effect Estimation
R Sato, M Yamada, H Kashima
arXiv preprint arXiv:2010.09157, 2020
12020
Learning to Sample Hard Instances for Graph Algorithms
R Sato, M Yamada, H Kashima
Asian Conference on Machine Learning, 503--518, 2019
12019
Supervised Tree-Wasserstein Distance
Y Takezawa, R Sato, M Yamada
arXiv preprint arXiv:2101.11520, 2021
2021
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Articles 1–10