Multitask Learning
Multitask Learning is a machine learning paradigm in which a single model simultaneously learns multiple related tasks by leveraging shared representations to improve generalization performance across tasks.
2.7K
Publications
226.1K
Citations
9.2K
Authors
1.8K
Institutions
Publications per year
2017–2026
2.1K
Parents
2.7K
Sinno Jialin Pan, Qiang Yang · IEEE Transactions on Knowledge and Data Engineering · 2009 · 23.2K citations
A unified architecture for natural language processing
Ronan Collobert, Jason Weston · 2008 · 5.2K citations
Adaptive Mixtures of Local Experts
Robert A. Jacobs, Michael I. Jordan, Steven J. Nowlan et al. · Neural Computation · 1991 · 5K citations
1–5 of 2.7K
9.2K
Hong Kong University of Science and Technology, Hong Kong SAR China
25 publications · 5.6K citations
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University of North Carolina at Chapel Hill, United States
18 publications · 2.3K citations
17
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1–5 of 9.2K
1.8K

Chinese Academy of Sciences
Beijing, China
43 h-index
282 publications
17.8K citations
Nanyang Technological University
Singapore, Singapore
38 h-index
115 publications
7.3K citations
Tsinghua University
Beijing, China
36 h-index
167 publications
8.6K citations
University of Chinese Academy of Sciences
Beijing, China
34 h-index
128 publications
12.9K citations
Carnegie Mellon University
Pittsburgh, United States
33 h-index
128 publications
7.4K citations
Stanford University
Stanford, United States
31 h-index
143 publications
12K citations
Google (United States)
Mountain View, United States
30 h-index
185 publications
28.2K citations

Zhejiang University
Hangzhou, China
29 h-index
136 publications
6.9K citations
1–8 of 1.8K
Repository · 299 publications · 22K citations
62
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Conference · 82 publications · 4.4K citations
39
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52 publications · 3.2K citations
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44 publications · 2.9K citations
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40 publications · 2.9K citations
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