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

Authors

9.2K

  1. YZ
    Yu Zhang

    Hong Kong University of Science and Technology, Hong Kong SAR China

    25 publications · 5.6K citations

    22

    h-index

  2. JY
    Jieping Ye

    Arizona State University, United States

    22 publications · 3K citations

    20

    h-index

  3. MP
    Massimiliano Pontil

    University College London, United Kingdom

    20 publications · 5.2K citations

    18

    h-index

  4. AH
    Ali H. Sayed

    University of California, Los Angeles, United States

    18 publications · 1.2K citations

    17

    h-index

  5. DS
    Dinggang Shen

    University of North Carolina at Chapel Hill, United States

    18 publications · 2.3K citations

    17

    h-index

1–5 of 9.2K

Venues

  1. arXiv (Cornell University)

    Repository · 299 publications · 22K citations

    62

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 82 publications · 4.4K citations

    39

    h-index

  3. 32

    h-index

  4. 30

    h-index

  5. IEEE Transactions on Cybernetics

    40 publications · 2.9K citations

    27

    h-index

1–5