1 citations · 1 across the 3 of their papers we have counts for
3 papers
stat.ML2023
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…
cs.LG2023★ 1 cited
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…
cs.LG2023
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…