72 citations · 197 across the 19 of their papers we have counts for
4 papers · 1 filter
Edge but not Least: Cross-View Graph Pooling
Xiaowei Zhou, Jie Yin, Ivor W. Tsang
Graph neural networks have emerged as a powerful model for graph representation learning to undertake graph-level prediction tasks. Various graph pooling methods have been develope…
Human-Understandable Decision Making for Visual Recognition
Xiaowei Zhou, Jie Yin, Ivor Tsang +1
The widespread use of deep neural networks has achieved substantial success in many tasks. However, there still exists a huge gap between the operating mechanism of deep learning m…
Unified Robust Training for Graph NeuralNetworks against Label Noise
Yayong Li, Jie yin, Ling Chen
Graph neural networks (GNNs) have achieved state-of-the-art performance for node classification on graphs. The vast majority of existing works assume that genuine node labels are a…
Towards Deepening Graph Neural Networks: A GNTK-based Optimization Perspective
Wei Huang, Yayong Li, Weitao Du +4
Graph convolutional networks (GCNs) and their variants have achieved great success in dealing with graph-structured data. Nevertheless, it is well known that deep GCNs suffer from…