Multi-task Learning
Multi-Task Learning is a machine learning paradigm in which a single model is trained to simultaneously perform multiple related tasks by leveraging shared representations and inductive biases to improve generalization across tasks.
5.8K
Publications
433K
Citations
18.1K
Authors
2.5K
Institutions
Publications per year
2017–2026
4.8K
5.8K
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 5.8K
18.1K
26
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26
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Hong Kong University of Science and Technology, Hong Kong SAR China
29 publications · 5.9K citations
25
h-index
University of North Carolina at Chapel Hill, United States
33 publications · 3.3K citations
24
h-index
20
h-index
1–5 of 18.1K
2.5K

Chinese Academy of Sciences
Beijing, China
56 h-index
534 publications
33.4K citations
Tsinghua University
Beijing, China
50 h-index
383 publications
28.5K citations
Carnegie Mellon University
Pittsburgh, United States
47 h-index
200 publications
14.6K citations
University of Chinese Academy of Sciences
Beijing, China
47 h-index
283 publications
18.7K citations
Nanyang Technological University
Singapore, Singapore
46 h-index
206 publications
12.9K citations

Peking University
Beijing, China
45 h-index
251 publications
23.6K citations
National University of Singapore
Singapore, Singapore
45 h-index
192 publications
16.5K citations

Hong Kong University of Science and Technology
Hong Kong, Hong Kong
42 h-index
158 publications
63.2K citations
1–8 of 2.5K
Repository · 795 publications · 55.1K citations
99
h-index
Conference · 166 publications · 12.3K citations
60
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73 publications · 7.6K citations
44
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TPAMI · 57 publications · 10.8K citations
38
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96 publications · 3.8K citations
36
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1–5