20 citations · 24 across the 7 of their papers we have counts for
4 papers · 1 filter
FedBRB: An Effective Solution to the Small-to-Large Scenario in Device-Heterogeneity Federated Learning
Ziyue Xu, Mingfeng Xu, Tianchi Liao +2
Recently, the success of large models has demonstrated the importance of scaling up model size. This has spurred interest in exploring collaborative training of large-scale models…
Capturing Fine-grained Semantics in Contrastive Graph Representation Learning
Lin Shu, Chuan Chen, Zibin Zheng
Graph contrastive learning defines a contrastive task to pull similar instances close and push dissimilar instances away. It learns discriminative node embeddings without supervise…
Decoupling anomaly discrimination and representation learning: self-supervised learning for anomaly detection on attributed graph
YanMing Hu, Chuan Chen, BoWen Deng +4
Anomaly detection on attributed graphs is a crucial topic for its practical application. Existing methods suffer from semantic mixture and imbalance issue because they mainly focus…
Multi-View Clustering from the Perspective of Mutual Information
Fu Lele, Zhang Lei, Wang Tong +3
Exploring the complementary information of multi-view data to improve clustering effects is a crucial issue in multi-view clustering. In this paper, we propose a novel model based…