Continuous Sequence Learning

Continuous Sequence Learning is a machine learning framework within sequence learning that enables models to incrementally acquire and adapt to new sequential patterns over time without forgetting previously learned knowledge, distinguishing it from batch or episodic sequence learning approaches.

18

Publications

1.3K

Citations

59

Authors

28

Institutions

Publications per year

2017–2026

10

Authors

59

  1. WD
    Werner Doyle

    New York University, United States

    2 publications · 194 citations

    2

    h-index

  2. AF
    Adeen Flinker

    New York University, United States

    2 publications · 194 citations

    2

    h-index

  3. SH
    Simon Henin

    City College of New York, United States

    2 publications · 194 citations

    2

    h-index

  4. PD
    Patricia Dugan

    New York University, United States

    2 publications · 194 citations

    2

    h-index

  5. NB
    Nicholas B. Turk‐Browne

    Yale University, United States

    2 publications · 194 citations

    2

    h-index

1–5 of 59

Venues

  1. Behavioral Neuroscience

    1 publications · 11 citations

    1

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  2. Journal of Cognitive Neuroscience

    1 publications · 48 citations

    1

    h-index

  3. 1

    h-index

  4. IEEE Transactions on Multimedia

    1 publications · 413 citations

    1

    h-index

  5. Cognitive Psychology

    1 publications · 20 citations

    1

    h-index

1–5