Continual Supervised Learning

Continual Supervised Learning is a machine learning paradigm within supervised learning wherein a model incrementally learns from a sequential stream of labeled data tasks while preserving knowledge from previous tasks to avoid catastrophic forgetting.

59

Publications

8.2K

Citations

260

Authors

117

Institutions

Publications per year

2017–2026

56

Authors

260

  1. VL
    Vincenzo Lomonaco

    University of Bologna, Italy

    4 publications · 92 citations

    4

    h-index

  2. AC
    Andrea Cossu

    University of Pisa, Italy

    2 publications · 41 citations

    2

    h-index

  3. DV
    David Vázquez

    Universitat Autònoma de Barcelona, Spain

    2 publications · 51 citations

    2

    h-index

  4. LS
    Luping Shi

    Tsinghua University, China

    2 publications · 55 citations

    2

    h-index

  5. MR
    Matthew Riemer

    IBM (United States), United States

    2 publications · 449 citations

    2

    h-index

1–5 of 260

Venues

  1. arXiv (Cornell University)

    Repository · 12 publications · 1.2K citations

    12

    h-index

  2. Information Sciences

    4 publications · 189 citations

    4

    h-index

  3. Expert Systems with Applications

    3 publications · 65 citations

    3

    h-index

  4. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 2 publications · 216 citations

    2

    h-index

  5. Neural Networks

    2 publications · 66 citations

    2

    h-index

1–5