213 citations · 268 across the 6 of their papers we have counts for
7 papers
Personalized Federated Learning via Heterogeneous Modular Networks
Tianchun Wang, Wei Cheng, Dongsheng Luo +5
Personalized Federated Learning (PFL) which collaboratively trains a federated model while considering local clients under privacy constraints has attracted much attention. Despite…
InfoGCL: Information-Aware Graph Contrastive Learning
Dongkuan Xu, Wei Cheng, Dongsheng Luo +2
Various graph contrastive learning models have been proposed to improve the performance of learning tasks on graph datasets in recent years. While effective and prevalent, these mo…
Unsupervised Document Embedding via Contrastive Augmentation
Dongsheng Luo, Wei Cheng, Jingchao Ni +8
We present a contrasting learning approach with data augmentation techniques to learn document representations in an unsupervised manner. Inspired by recent contrastive self-superv…
Attentive Social Recommendation: Towards User And Item Diversities
Dongsheng Luo, Yuchen Bian, Xiang Zhang +1
Social recommendation system is to predict unobserved user-item rating values by taking advantage of user-user social relation and user-item ratings. However, user/item diversities…
Learning to Drop: Robust Graph Neural Network via Topological Denoising
Dongsheng Luo, Wei Cheng, Wenchao Yu +4
Graph Neural Networks (GNNs) have shown to be powerful tools for graph analytics. The key idea is to recursively propagate and aggregate information along edges of the given graph.…
Parameterized Explainer for Graph Neural Network
Dongsheng Luo, Wei Cheng, Dongkuan Xu +4
Despite recent progress in Graph Neural Networks (GNNs), explaining predictions made by GNNs remains a challenging open problem. The leading method independently addresses the loca…