Meta-learning Theory

Meta-Learning Theory is a formal academic construct that belongs to the category of theoretical frameworks in machine learning, specifically distinguished by its focus on principles and mechanisms that enable learning algorithms to acquire or improve their own learning processes across tasks through prior experience.

364

Publications

19.3K

Citations

1.2K

Authors

418

Institutions

Publications per year

2017–2026

247

Authors

1.2K

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

    Universidade de São Paulo, Brazil

    10 publications · 557 citations

    10

    h-index

  2. CS
    Carlos Soares

    Universidade do Porto, Portugal

    9 publications · 543 citations

    9

    h-index

  3. CF
    Chelsea Finn

    University of California, Berkeley, United States

    7 publications · 451 citations

    7

    h-index

  4. CG
    Christophe Giraud-Carrier

    Brigham Young University, United States

    6 publications · 618 citations

    6

    h-index

  5. PB
    Pavel Brazdil

    Universidade do Porto, Portugal

    6 publications · 479 citations

    6

    h-index

1–5 of 1.2K

Venues

  1. arXiv (Cornell University)

    Repository · 65 publications · 3.5K citations

    29

    h-index

  2. Neurocomputing

    15 publications · 1.3K citations

    13

    h-index

  3. Information Sciences

    9 publications · 417 citations

    9

    h-index

  4. Neural Networks

    6 publications · 447 citations

    6

    h-index

  5. IEEE Access

    6 publications · 211 citations

    6

    h-index

1–5