Low-rank Multi-task Learning
Low-Rank Multi-Task Learning is a multi-task learning framework that leverages a low-rank assumption on the parameter space to model task relatedness by capturing shared structures among tasks through a compressed, lower-dimensional representation.
39
Publications
5.2K
Citations
129
Authors
76
Institutions
Publications per year
2017–2026
22
Parents
39
A Survey on Multi-Task Learning
Yu Zhang, Qiang Yang · IEEE Transactions on Knowledge and Data Engineering · 2021 · 2.2K citations
A Survey on Multi-Task Learning
Yu Zhang, Qiang Yang · arXiv (Cornell University) · 2017 · 621 citations · Full text
Multimodal Face-Pose Estimation With Multitask Manifold Deep Learning
Chaoqun Hong, Jun Yu, Jian Zhang et al. · IEEE Transactions on Industrial Informatics · 2018 · 309 citations
1–5 of 39
129
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4
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Hong Kong University of Science and Technology, Hong Kong SAR China
3 publications · 2.9K citations
3
h-index
2
h-index
2
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1–5 of 129
76

Chinese Academy of Sciences
Beijing, China
4 h-index
10 publications
193 citations
Arizona State University
Tempe, United States
3 h-index
9 publications
1.6K citations
Michigan State University
East Lansing, United States
3 h-index
5 publications
158 citations
Tokyo Institute of Technology
Tokyo, Japan
3 h-index
4 publications
115 citations
University of Maryland, College Park
College Park, United States
3 h-index
5 publications
675 citations
The University of Texas at San Antonio
San Antonio, United States
3 h-index
3 publications
347 citations

Hong Kong University of Science and Technology
Hong Kong, Hong Kong
3 h-index
4 publications
3.5K citations
University of Chinese Academy of Sciences
Beijing, China
2 h-index
3 publications
67 citations
1–8 of 76
4 publications · 128 citations
4
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Repository · 3 publications · 670 citations
3
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TPAMI · 2 publications · 498 citations
2
h-index
TKDE · 2 publications · 2.2K citations
2
h-index
1 publications · 12 citations
1
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1–5