23 citations · 47 across the 7 of their papers we have counts for
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3 papers · 1 filter
cs.LG2020★ 2 cited
Stacked Graph Filter
Hoang NT, Takanori Maehara, Tsuyoshi Murata
We study Graph Convolutional Networks (GCN) from the graph signal processing viewpoint by addressing a difference between learning graph filters with fully connected weights versus…
stat.ML2020
MetAL: Active Semi-Supervised Learning on Graphs via Meta Learning
Kaushalya Madhawa, Tsuyoshi Murata
The objective of active learning (AL) is to train classification models with less number of labeled instances by selecting only the most informative instances for labeling. The AL…
cs.LG2020
Graph Convolutional Networks for Graphs Containing Missing Features
Hibiki Taguchi, Xin Liu, Tsuyoshi Murata
Graph Convolutional Network (GCN) has experienced great success in graph analysis tasks. It works by smoothing the node features across the graph. The current GCN models overwhelmi…