Representation Learning

Representation Learning is a machine learning paradigm that belongs to the broader class of feature learning methods, distinguished by its focus on automatically discovering the optimal data representations required for feature detection or classification from raw inputs.

70.1K

Publications

7.6M

Citations

135.6K

Authors

10.7K

Institutions

Publications per year

2017–2026

51.1K

Authors

135.6K

  1. DT
    Dacheng Tao

    The University of Sydney, Australia

    260 publications · 27.7K citations

    92

    h-index

  2. YY
    Yi Yang

    University of Technology Sydney, Australia

    191 publications · 34.6K citations

    83

    h-index

  3. YB
    Yoshua Bengio

    Université de Montréal, Canada

    170 publications · 136.2K citations

    81

    h-index

  4. XW
    Xiaogang Wang

    European Organization for Nuclear Research, Switzerland

    127 publications · 42.9K citations

    78

    h-index

  5. SY
    Shuicheng Yan

    National University of Singapore, Singapore

    197 publications · 33.4K citations

    73

    h-index

1–5 of 135.6K

Venues

  1. arXiv (Cornell University)

    Repository · 8.2K publications · 967.1K citations

    362

    h-index

  2. IEEE Transactions on Pattern Analysis and Machine Intelligence

    TPAMI · 1K publications · 312.2K citations

    207

    h-index

  3. IEEE Transactions on Image Processing

    1.1K publications · 119.7K citations

    160

    h-index

  4. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 1.3K publications · 124.4K citations

    156

    h-index

  5. IEEE Access

    1.9K publications · 114.6K citations

    144

    h-index

1–5