69 citations · 81 across the 8 of their papers we have counts for
12 papers
FedKNOW: Federated Continual Learning with Signature Task Knowledge Integration at Edge
Yaxin Luopan, Rui Han, Qinglong Zhang +2
Deep Neural Networks (DNNs) have been ubiquitously adopted in internet of things and are becoming an integral of our daily life. When tackling the evolving learning tasks in real w…
Scaling Up Maximal k-plex Enumeration
Qiangqiang Dai, Rong-Hua Li, Hongchao Qin +2
Finding all maximal -plexes on networks is a fundamental research problem in graph analysis due to many important applications, such as community detection, biological graph ana…
Deep Unsupervised Active Learning on Learnable Graphs
Handong Ma, Changsheng Li, Xinchu Shi +2
Recently deep learning has been successfully applied to unsupervised active learning. However, current method attempts to learn a nonlinear transformation via an auto-encoder while…
Efficient Top-k Ego-Betweenness Search
Qi Zhang, Rong-Hua Li, Minjia Pan +3
Betweenness centrality, measured by the number of times a vertex occurs on all shortest paths of a graph, has been recognized as a key indicator for the importance of a vertex in t…
Semantic Distribution-aware Contrastive Adaptation for Semantic Segmentation
Shuang Li, Binhui Xie, Bin Zang +4
Domain adaptive semantic segmentation refers to making predictions on a certain target domain with only annotations of a specific source domain. Current state-of-the-art works sugg…
Generalized Domain Conditioned Adaptation Network
Shuang Li, Binhui Xie, Qiuxia Lin +3
Domain Adaptation (DA) attempts to transfer knowledge learned in the labeled source domain to the unlabeled but related target domain without requiring large amounts of target supe…