Q
Qiang Yang
Researcher at Hong Kong University of Science and Technology
Publications - 1795
Citations - 96705
Qiang Yang is an academic researcher from Hong Kong University of Science and Technology. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 112, co-authored 1117 publications receiving 71540 citations. Previous affiliations of Qiang Yang include University of London & Zhejiang University of Technology.
Papers
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Journal ArticleDOI
Tunable quantum criticalities in an isospin extended Hubbard model simulator
Qiao Li,Bin Cheng,Moyu Chen,Bo-Min Xie,Yong Qin Xie,Pengfei Wang,Fanqiang Chen,Zenglin Liu,Kenji Watanabe,Takashi Taniguchi,Shi-Jun Liang,Da Wang,Chenjie Wang,Qiang Yang,Jianpeng Liu,Feng Miao +15 more
TL;DR: In this article , a quantum two-stage criticality with spin-valley isospins arising in chiral-stacked twisted double bilayer graphene (cTDBG) was observed.
Patent
Method and device for identifying target object in image
TL;DR: In this article, a method and a device for identifying a target object in an image is described, which includes the following steps: extracting feature data from an image and converting the extracted feature data into a unified expression; automatically clustering the features of the image according to the feature data after unified expression and a historical clustering result; categorizing a known classification included in the automatic clustering results as a corresponding known classification so as to identify a known target object.
Patent
Feature selection method, device and storage medium based on federation training
TL;DR: In this paper, a feature selection method based on federation training is proposed, which comprises the following steps: federating two aligned training samples by using XGboost algorithm to construct a gradient lifting tree model, and feature ranking being performed based on the score of each feature and the ranking result being output for feature selection.
Posted Content
Graph Random Neural Network.
TL;DR: This work proposes the consistency regularization for Grand by leveraging the distributional consistency of unlabeled nodes in multiple augmentations, improving the generalization capacity of the model.