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
Mapping the Trophic State Index of Eastern Lakes in China using an Empirical Model and Sentinel-2 Imagery Data
Sijia Li,Fangfang Chen,Kaishan Song,Ge Liu,Hui-Hui Tao,Shiqi Xu,Xiang Wang,Qiang Yang,Guangyi Mu +8 more
TL;DR: Wang et al. as discussed by the authors collected in situ lake samples (N = 431) from typical lakes to match Sentinel-2 multi-spectral instrument (MSI) imagery data using the Case 2 Regional Coast Color processor.
Rating Prediction with Informative Ensemble of Multi-Resolution Dynamic Models.
TL;DR: An informative ensemble framework is designed, which considers additional meta features when making predictions for a particular pair of user and item and adopts time series based parameterizations and models user/item drifting behaviors at multiple temporal resolutions.
Book ChapterDOI
Automatically abstracting the effects of operators
Eugene Fink,Qiang Yang +1 more
TL;DR: A theorem is presented that describes the necessary and sufficient conditions for a planner to be complete, when guided by primary effects, and produces finer-grained abstraction hierarchies than ALPINE.
Proceedings ArticleDOI
Learning action models for multi-agent planning
TL;DR: This paper presents an algorithm to learn action models for multi-agent planning systems from a set of input plan traces called Lammas, which attempts to satisfy three kinds of constraints simultaneously using a weighted maximum satisfiability model known as MAX-SAT and converts the solution into action models.
Proceedings Article
Plan Mining by Divide-and-Conquer
Jiawei Han,Qiang Yang,Edward Kim +2 more
TL;DR: This paper presents a method for mining patterns of successful actions in a large planbase using a divide and conquer strategy that exploits multi dimensional generalization of sequences of actions and extracts the inherent hierarchical structure and sequential patterns of plans at levels of abstraction.