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Stan Z. Li
Researcher at Westlake University
Publications - 625
Citations - 49737
Stan Z. Li is an academic researcher from Westlake University. The author has contributed to research in topics: Facial recognition system & Computer science. The author has an hindex of 97, co-authored 532 publications receiving 41793 citations. Previous affiliations of Stan Z. Li include Microsoft & Macau University of Science and Technology.
Papers
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Journal Article
Deep Clustering and Representation Learning that Preserves Geometric Structures
TL;DR: Experimental results on various datasets show that the proposed DCRL framework leads to comparable performances to current state-of-the-art deep clustering algorithms, yet exhibits superior performance for downstream tasks.
Journal ArticleDOI
DiffSDS: A language diffusion model for protein backbone inpainting under geometric conditions and constraints
Zhan Gao,Cheng Tan,Stan Z. Li +2 more
TL;DR: In this paper , Zhang et al. proposed to use a hidden direction space (ADS) upon the language model, converting invariant backbone angles into equivalent direction vectors and preserving the simplicity.
Proceedings ArticleDOI
Content-based audio segmentation content-based audio segmentation
TL;DR: Experiments on a database composed of clips of 14870 seconds in total length show that the average accuracy rate for the SVM method is much better than that of the traditional Euclidean distance based (nearest neighbor) method.
Book ChapterDOI
GraphMixup: Improving Class-Imbalanced Node Classification by Reinforcement Mixup and Self-supervised Context Prediction
TL;DR: Wu et al. as mentioned in this paper proposed GraphMixup, a novel mixup-based framework for improving class-imbalanced node classification on graphs, which constructs semantic relation spaces that allow Feature Mixup to be performed at the semantic level.
Journal ArticleDOI
Beyond Homophily and Homogeneity Assumption: Relation-Based Frequency Adaptive Graph Neural Networks.
TL;DR: Wu et al. as discussed by the authors proposed a relation-based frequency adaptive GNN (RFA-GNN) to handle both heterophily and heterogeneity in a unified framework.