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

Authors

18.1K

  1. JY
    Jieping Ye

    Arizona State University, United States

    33 publications · 4.5K citations

    26

    h-index

  2. DT
    Dacheng Tao

    The University of Sydney, Australia

    36 publications · 3.3K citations

    26

    h-index

  3. YZ
    Yu Zhang

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

    29 publications · 5.9K citations

    25

    h-index

  4. DS
    Dinggang Shen

    University of North Carolina at Chapel Hill, United States

    33 publications · 3.3K citations

    24

    h-index

  5. MP
    Massimiliano Pontil

    University College London, United Kingdom

    24 publications · 5.8K citations

    20

    h-index

1–5 of 18.1K

Venues

  1. arXiv (Cornell University)

    Repository · 795 publications · 55.1K citations

    99

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 166 publications · 12.3K citations

    60

    h-index

  3. IEEE Transactions on Image Processing

    73 publications · 7.6K citations

    44

    h-index

  4. IEEE Transactions on Pattern Analysis and Machine Intelligence

    TPAMI · 57 publications · 10.8K citations

    38

    h-index

  5. IEEE Access

    96 publications · 3.8K citations

    36

    h-index

1–5