29 citations · 56 across the 18 of their papers we have counts for
4 papers · 1 filter
NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning
Jiayu Xu, Junbiao Pang
Learning from Noisy Labels (LNL) remains a fundamental challenge in deep learning because real-world datasets often contain corrupted annotations. Most existing methods rely on lab…
Simplifying Node Classification on Heterophilous Graphs with Compatible Label Propagation
Zhiqiang Zhong, Sergey Ivanov, Jun Pang
Graph Neural Networks (GNNs) have been predominant for graph learning tasks; however, recent studies showed that a well-known graph algorithm, Label Propagation (LP), combined with…
Personalised Meta-path Generation for Heterogeneous GNNs
Zhiqiang Zhong, Cheng-Te Li, Jun Pang
Recently, increasing attention has been paid to heterogeneous graph representation learning (HGRL), which aims to embed rich structural and semantic information in heterogeneous in…
Hierarchical Message-Passing Graph Neural Networks
Zhiqiang Zhong, Cheng-Te Li, Jun Pang
Graph Neural Networks (GNNs) have become a prominent approach to machine learning with graphs and have been increasingly applied in a multitude of domains. Nevertheless, since most…