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.
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Proceedings ArticleDOI
Separate to Adapt: Open Set Domain Adaptation via Progressive Separation
TL;DR: The approach adopts a coarse-to-fine weighting mechanism to progressively separate the samples of unknown and known classes, and simultaneously weigh their importance on feature distribution alignment, which allows openness-robust open set domain adaptation, which can be adaptive to a variety of openness in the target domain.
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SNPHarvester: a filtering-based approach for detecting epistatic interactions in genome-wide association studies
TL;DR: SNPHarvester creates multiple paths in which the visited SNP groups tend to be statistically associated with diseases, and then harvests those significant SNP groups which pass the statistical tests, which greatly reduces the number of SNPs.
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A Multi Time-Scale and Multi Energy-Type Coordinated Microgrid Scheduling Solution—Part I: Model and Methodology
TL;DR: In this article, the authors proposed a multi-scale cooling and electricity coordinated schedule for optimal microgrid operation, which achieves an integrated optimization for multi-energy type supply, and makes the MG be controllable as seen from the main grid.
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Rate-dependent interface capture beyond the coffee-ring effect.
TL;DR: This simple, robust drying regime will provide a versatile strategy to control the droplet deposition morphology, and a novel direction of interface assembling for fabricating superlattices and high quality photonic crystal patterns.
Proceedings ArticleDOI
Context-aware query classification
TL;DR: This paper incorporates context information into the problem of query classification by using conditional random field (CRF) models and shows that it can improve the F1 score by 52% as compared to other state-of-the-art baselines.