Multi-task Optimization
Multi-Task Optimization is a computational learning framework that jointly optimizes multiple related tasks by leveraging their shared representations and inter-task dependencies to improve generalization performance compared to learning each task separately.
516
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36.1K
Citations
2K
Authors
535
Institutions
Publications per year
2017–2026
427
Parents
516
Convex multi-task feature learning
Andreas A. Argyriou, Theodoros Evgeniou, Massimiliano Pontil · Machine Learning · 2008 · 1.4K citations · Full text
Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts
Jiaqi Ma, Zhe Zhao, Xinyang Yi et al. · 2018 · 1.1K citations · Full text
Planning-oriented Autonomous Driving
Yihan Hu, Jiazhi Yang, Li Chen et al. · 2023 · 713 citations
End-to-End Blind Image Quality Assessment Using Deep Neural Networks
Kede Ma, Wentao Liu, Kai Zhang et al. · IEEE Transactions on Image Processing · 2017 · 620 citations
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535

South China University of Technology
Guangzhou, China
20 h-index
44 publications
3.1K citations

Xidian University
Xi'an, China
20 h-index
78 publications
4.5K citations

Shenzhen University
Shenzhen, China
19 h-index
68 publications
3.1K citations
Nanyang Technological University
Singapore, Singapore
16 h-index
38 publications
4.4K citations

Chinese Academy of Sciences
Beijing, China
15 h-index
50 publications
3.5K citations
Tsinghua University
Beijing, China
14 h-index
43 publications
3.7K citations
Carnegie Mellon University
Pittsburgh, United States
13 h-index
29 publications
1.9K citations

Hong Kong Polytechnic University
Hong Kong, Hong Kong
12 h-index
24 publications
1K citations
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Repository · 66 publications · 3.4K citations
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33 publications · 2.9K citations
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17 publications · 1.5K citations
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13 publications · 543 citations
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Conference · 12 publications · 1.1K citations
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