38 citations · 81 across the 27 of their papers we have counts for
6 papers · 1 filter
Domain-Aware Hierarchical Contrastive Learning for Semi-Supervised Generalization Fault Diagnosis
Junyu Ren, Wensheng Gan, Philip S Yu
Fault diagnosis under unseen operating conditions remains highly challenging when labeled data are scarce. Semi-supervised domain generalization fault diagnosis (SSDGFD) provides a…
Graph Attention-based Adaptive Transfer Learning for Link Prediction
Huashen Lu, Wensheng Gan, Guoting Chen +2
Graph neural networks (GNNs) have brought revolutionary advancements to the field of link prediction (LP), providing powerful tools for mining potential relationships in graphs. Ho…
Pure Node Selection for Imbalanced Graph Node Classification
Fanlong Zeng, Wensheng Gan, Jiayang Wu +1
The problem of class imbalance refers to an uneven distribution of quantity among classes in a dataset, where some classes are significantly underrepresented compared to others. Cl…
GraphIFE: Rethinking Graph Imbalance Node Classification via Invariant Learning
Fanlong Zeng, Wensheng Gan, Philip S. Yu +1
The class imbalance problem refers to the disproportionate distribution of samples across different classes within a dataset, where the minority classes are significantly underrepr…
Graph Diffusion Network for Drug-Gene Prediction
Jiayang Wu, Wensheng Gan, Philip S. Yu
Predicting drug-gene associations is crucial for drug development and disease treatment. While graph neural networks (GNN) have shown effectiveness in this task, they face challeng…
Mixture of Experts (MoE): A Big Data Perspective
Wensheng Gan, Zhenyao Ning, Zhenlian Qi +1
As the era of big data arrives, traditional artificial intelligence algorithms have difficulty processing the demands of massive and diverse data. Mixture of experts (MoE) has show…