J
Junzhou Huang
Researcher at Tencent
Publications - 443
Citations - 18548
Junzhou Huang is an academic researcher from Tencent. The author has contributed to research in topics: Computer science & Graph (abstract data type). The author has an hindex of 54, co-authored 388 publications receiving 12110 citations. Previous affiliations of Junzhou Huang include University of Texas at Austin & University of Maryland, Baltimore.
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DropEdge: Towards Deep Graph Convolutional Networks on Node Classification.
TL;DR: DropEdge is a general skill that can be equipped with many other backbone models (e.g. GCN, ResGCN, GraphSAGE, and JKNet) for enhanced performance and consistently improves the performance on a variety of both shallow and deep GCNs.
Journal ArticleDOI
The Benefit of Group Sparsity
Junzhou Huang,Tong Zhang +1 more
TL;DR: The result shows that group Lasso is superior to standard Lasso for strongly group-sparse signals, and provides a convincing theoretical justification for using group sparse regularization when the underlying group structure is consistent with the data.
Proceedings ArticleDOI
Robust tracking using local sparse appearance model and K-selection
TL;DR: A robust tracking algorithm using a local sparse appearance model (SPT), a static sparse dictionary and a dynamically online updated basis distribution model the target appearance, and a new dictionary learning algorithm with a locally constrained sparse representation, called K-Selection are developed.
Proceedings ArticleDOI
Graph Convolutional Networks for Temporal Action Localization
TL;DR: Zhang et al. as mentioned in this paper exploit the proposal-proposal relations using GraphConvolutional Networks (GCNs) to exploit the context information for each proposal and the correlations between distinct actions.
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Learning with Structured Sparsity
TL;DR: This paper investigates a new learning formulation called structured sparsity, which is a natural extension of the standard sparsity concept in statistical learning and compressive sensing by allowing arbitrary structures on the feature set, which generalizes the group sparsity idea.