Continual Meta-learning

Continual Meta-Learning is a meta-learning paradigm that enables a model to progressively adapt and accumulate knowledge across sequentially encountered tasks while retaining the ability to generalize to new tasks without forgetting previously acquired meta-knowledge.

42

Publications

1.9K

Citations

164

Authors

69

Institutions

Publications per year

2017–2026

38

Authors

164

  1. TM
    Tsendsuren Munkhdalai

    Chungbuk National University, South Korea

    3 publications · 436 citations

    3

    h-index

  2. HY
    Hong Yu

    University of Massachusetts Amherst, United States

    3 publications · 436 citations

    3

    h-index

  3. GG
    Gunshi Gupta

    International Institute of Information Technology, Hyderabad, India

    2 publications · 53 citations

    2

    h-index

  4. PR
    Pau Rodríguez

    Universitat Autònoma de Barcelona, Spain

    2 publications · 25 citations

    2

    h-index

  5. LC
    Laurent Charlin

    HEC Montréal, Canada

    2 publications · 25 citations

    2

    h-index

1–5 of 164

Venues

  1. arXiv (Cornell University)

    Repository · 14 publications · 763 citations

    13

    h-index

  2. Symmetry

    1 publications · 16 citations

    1

    h-index

  3. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 1 publications · 26 citations

    1

    h-index

  4. Neuron

    1 publications · 55 citations

    1

    h-index

  5. PubMed

    Repository · 1 publications · 25 citations

    1

    h-index

1–5