Related work

The foundational work on continual learning, 1959 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

5 papers of 11,817Sort Recent · Most cited
  1. 2021
    Meta-learning synaptic plasticity and memory addressing for continual familiarity detectionDanil Tyulmankov, Guangyu Robert Yang, L. F. AbbottNeuron · Columbia University · Allen Institute for Brain Science · +1
  2. 2021
    Beyond gradients: Factorized, geometric control of interference and generalizationDavid M. Scott, Michael J. FrankbioRxiv · Brown University · Allen Institute for Brain Science
  3. 2021
    A biologically inspired architecture with switching units can learn to generalize across backgroundsDoris Voina, Eric Shea‐Brown, Ştefan MihalaşbioRxiv · University of Washington · University of Washington Applied Physics Laboratory · +2
  4. 2018
    DynMat, a network that can learn after learningJung H. LeePubMed · Allen Institute for Brain Science · Allen Institute
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  5. 2017
    Reminders of past choices bias decisions for reward in humansAaron M. Bornstein, Mel Win Khaw, Daphna Shohamy, Nathaniel D. DawNature Communications · Princeton University · Columbia University · +2
About this index

We keep this list because we read the field and wanted one place to see it. It covers work on continual learning itself, in the core areas of machine learning, and leaves out papers that apply it inside another field, such as medical imaging or fault diagnosis. It is seeded from the community lists kept by ContinualAI and by Xialei Liu, then filled out from OpenAlex, and every week a script looks for new papers on OpenAlex and arXiv. A model reads each candidate and decides whether it belongs; a person reviews the additions before they go live. Authors and affiliations come from OpenAlex, so a recent preprint can lack its institutions for a week or two.

Missing something, or filed under the wrong venue? Write to hello@unify.ai with the arXiv id or DOI.