Joint Representation Learning
Joint Representation Learning is a computational approach that, as a member of the class of multi-omics integration methods, constructs a unified latent representation by simultaneously modeling shared and complementary patterns across multiple biological data modalities to enable downstream analyses.
850
Publications
62.1K
Citations
3K
Authors
801
Institutions
Publications per year
2017–2026
657
Variants
Jrl
Parents
850
VarifocalNet: An IoU-aware Dense Object Detector
Haoyang Zhang, Ying Wang, Feras Dayoub et al. · 2021 · 1K citations
Joint Discriminative and Generative Learning for Person Re-Identification
Zhedong Zheng, Xiaodong Yang, Zhiding Yu et al. · 2019 · 859 citations
1–5 of 850
3K
University of North Carolina at Chapel Hill, United States
17 publications · 2.4K citations
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801

Chinese Academy of Sciences
Beijing, China
31 h-index
124 publications
10.4K citations
Tsinghua University
Beijing, China
21 h-index
68 publications
4.9K citations
University of Chinese Academy of Sciences
Beijing, China
20 h-index
73 publications
7.8K citations

Harbin Institute of Technology
Harbin, China
19 h-index
45 publications
3.2K citations

Xidian University
Xi'an, China
19 h-index
75 publications
4.1K citations

Sun Yat-sen University
Guangzhou, China
18 h-index
59 publications
5.8K citations

University of Electronic Science and Technology of China
Chengdu, China
17 h-index
42 publications
3.7K citations

Peking University
Beijing, China
16 h-index
50 publications
2.9K citations
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Repository · 86 publications · 4.5K citations
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Conference · 29 publications · 1.8K citations
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27 publications · 2.5K citations
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TPAMI · 22 publications · 4.7K citations
21
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17 publications · 992 citations
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