19 citations · 20 across the 5 of their papers we have counts for
5 papers
Bridging Trustworthiness and Open-World Learning: An Exploratory Neural Approach for Enhancing Interpretability, Generalization, and Robustness
Shide Du, Zihan Fang, Shiyang Lan +4
As researchers strive to narrow the gap between machine intelligence and human through the development of artificial intelligence technologies, it is imperative that we recognize t…
Attributed Multi-order Graph Convolutional Network for Heterogeneous Graphs
Zhaoliang Chen, Zhihao Wu, Luying Zhong +3
Heterogeneous graph neural networks aim to discover discriminative node embeddings and relations from multi-relational networks.One challenge of heterogeneous graph learning is the…
AGNN: Alternating Graph-Regularized Neural Networks to Alleviate Over-Smoothing
Zhaoliang Chen, Zhihao Wu, Zhenghong Lin +3
Graph Convolutional Network (GCN) with the powerful capacity to explore graph-structural data has gained noticeable success in recent years. Nonetheless, most of the existing GCN-b…
Multi-view Graph Convolutional Networks with Differentiable Node Selection
Zhaoliang Chen, Lele Fu, Shunxin Xiao +3
Multi-view data containing complementary and consensus information can facilitate representation learning by exploiting the intact integration of multi-view features. Because most…
Unsupervised Deep Discriminant Analysis Based Clustering
Jinyu Cai, Wenzhong Guo, Jicong Fan
This work presents an unsupervised deep discriminant analysis for clustering. The method is based on deep neural networks and aims to minimize the intra-cluster discrepancy and max…