Meta-learning Optimization

Meta-Learning Optimization is a computational framework within machine learning that enables the automatic adaptation of optimization algorithms by leveraging knowledge acquired across multiple learning tasks to improve learning efficiency and generalization in novel tasks.

207

Publications

14.6K

Citations

774

Authors

294

Institutions

Publications per year

2017–2026

172

Authors

774

  1. AC
    André C. P. L. F. de Carvalho

    Universidade de São Paulo, Brazil

    9 publications · 550 citations

    9

    h-index

  2. CS
    Carlos Soares

    Universidade do Porto, Portugal

    7 publications · 531 citations

    7

    h-index

  3. RB
    Ricardo B. C. Prudêncio

    Universidade Federal de Pernambuco, Brazil

    4 publications · 340 citations

    4

    h-index

  4. JS
    Jascha Sohl‐Dickstein

    Google (United States), United States

    4 publications · 157 citations

    4

    h-index

  5. XS
    Xingyou Song

    Google (United States), United States

    3 publications · 109 citations

    3

    h-index

1–5 of 774

Venues

  1. arXiv (Cornell University)

    Repository · 28 publications · 827 citations

    17

    h-index

  2. Neurocomputing

    11 publications · 1.1K citations

    11

    h-index

  3. IEEE Access

    8 publications · 177 citations

    7

    h-index

  4. Information Sciences

    4 publications · 200 citations

    4

    h-index

  5. Neural Networks

    4 publications · 142 citations

    4

    h-index

1–5