6 citations · 18 across the 7 of their papers we have counts for
10 papers
Informative Pseudo-Labeling for Graph Neural Networks with Few Labels
Yayong Li, Jie Yin, Ling Chen
Graph Neural Networks (GNNs) have achieved state-of-the-art results for semi-supervised node classification on graphs. Nevertheless, the challenge of how to effectively learn GNNs…
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…
SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs
Yayong Li, Jie Yin, Ling Chen
Active learning (AL) on attributed graphs has received increasing attention with the prevalence of graph-structured data. Although AL has been widely studied for alleviating label…
Latent Adversarial Defence with Boundary-guided Generation
Xiaowei Zhou, Ivor W. Tsang, Jie Yin
Deep Neural Networks (DNNs) have recently achieved great success in many tasks, which encourages DNNs to be widely used as a machine learning service in model sharing scenarios. Ho…