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Wei Tang
Researcher at University of Texas at Austin
Publications - 5
Citations - 457
Wei Tang is an academic researcher from University of Texas at Austin. The author has contributed to research in topics: Computer science & Social network analysis. The author has an hindex of 2, co-authored 3 publications receiving 403 citations.
Papers
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Proceedings ArticleDOI
Clustering with Multiple Graphs
TL;DR: Experiments on SIAM journal data show that LMF can improve the clustering accuracy through fusing multiple sources of information with several models, and LMF yields superior or competitive results compared to other graph-based clustering methods.
Proceedings ArticleDOI
Supervised Link Prediction Using Multiple Sources
TL;DR: A supervised learning framework that can effectively and efficiently learn the dynamics of social networks in the presence of auxiliary networks, a feature design scheme for constructing a rich variety of path-based features using multiple sources, and an effective feature selection strategy based on structured sparsity are presented.
Journal ArticleDOI
Multi-Instance Partial-Label Learning: Towards Exploiting Dual Inexact Supervision
TL;DR: In this paper , a tailored algorithm named Multi-Instance Partial-Label Learning with Gaussian Processes (MIPL G P ) is proposed, which assigns each instance with a candidate label set in an augmented label space, then transforms the candidate labels into a logarithmic space, and last induces a model based on the Gaussian processes.
Journal ArticleDOI
Disambiguated Attention Embedding for Multi-Instance Partial-Label Learning
TL;DR: DEMIPL as mentioned in this paper employs a disambiguation attention mechanism to aggregate a multi-instance bag into a single vector representation, followed by a momentum-based disambIGuation strategy to identify the ground-truth label from the candidate label set.