Q
Qing He
Researcher at Chinese Academy of Sciences
Publications - 393
Citations - 10520
Qing He is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Computer science & Support vector machine. The author has an hindex of 40, co-authored 346 publications receiving 5956 citations. Previous affiliations of Qing He include Xiangtan University & University of Texas at Austin.
Papers
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Journal ArticleDOI
A Comprehensive Survey on Transfer Learning
TL;DR: Transfer learning aims to improve the performance of target learners on target domains by transferring the knowledge contained in different but related source domains as discussed by the authors, in which the dependence on a large number of target-domain data can be reduced for constructing target learners.
Book ChapterDOI
Parallel K-Means Clustering Based on MapReduce
Weizhong Zhao,Huifang Ma,Qing He +2 more
TL;DR: This paper proposes a parallel k -means clustering algorithm based on MapReduce, which is a simple yet powerful parallel programming technique and demonstrates that the proposed algorithm can scale well and efficiently process large datasets on commodity hardware.
Journal ArticleDOI
Deep Subdomain Adaptation Network for Image Classification
TL;DR: This work presents a deep subdomain adaptation network (DSAN) that learns a transfer network by aligning the relevant subdomain distributions of domain-specific layer activations across different domains based on a local maximum mean discrepancy (LMMD).
Proceedings Article
Supervised representation learning: transfer learning with deep autoencoders
TL;DR: This paper proposes a supervised representation learning method based on deep autoencoders for transfer learning that consists of an embedding layer and a label encoding layer that minimize the difference between domains explicitly and encode label information in learning the representation.
Journal ArticleDOI
A survey on knowledge graph-based recommender systems
Qingyu Guo,Qingyu Guo,Fuzhen Zhuang,Chuan Qin,Chuan Qin,Hengshu Zhu,Xing Xie,Xing Xie,Hui Xiong,Qing He,Qing He +10 more
TL;DR: In this paper, the authors provide a focused survey on KG-based recommender system via a holistic perspective of both technologies and applications, and present their opinions on the prospects of KG based recommender systems and suggest some future research directions.