Related work

The foundational work on continual learning, 1988 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

6 papers of 7,070Sort Recent · Most cited
  1. 2023
    Correction to TINS 1828 Contributions by metaplasticity to solving the Catastrophic Forgetting Problem: (Trends in Neurosciences, 45:9 p:656-666, 2022).Peter Jedlička, Matúš Tomko, Anthony Robins, Wickliffe C. AbrahamTrends in Neurosciences · Goethe University Frankfurt · Justus-Liebig-Universität Gießen · +5
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  2. 2022
    Contributions by metaplasticity to solving the Catastrophic Forgetting Problem.Peter Jedlička, Matúš Tomko, Anthony Robins, Wickliffe C. AbrahamTrends in Neurosciences · Goethe University Frankfurt · Justus-Liebig-Universität Gießen · +6
  3. 2020
    Pseudo-Rehearsal: Achieving Deep Reinforcement Learning without Catastrophic ForgettingCraig Atkinson, Brendan McCane, Lech Szymanski, Anthony RobinsNeurocomputing · University of Otago
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  4. 1999
    Catastrophic forgetting in simple networks: an analysis of the pseudorehearsal solution.Marcus Frean, Anthony RobinsNetwork Computation in Neural Systems · Victoria University of Wellington · University of Otago
  5. 1998
    Local Learning Algorithms for Sequential Tasks in Neural NetworksAnthony Robins, Marcus FreanJournal of Advanced Computational Intelligence and Intell… · University of Otago · The University of Queensland
  6. 1995
    Catastrophic Forgetting, Rehearsal and PseudorehearsalAnthony RobinsConnection Science · University of Otago
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 lists only papers we have a reason to trust: published at a venue like NeurIPS, ICML, ICLR, CVPR or TPAMI, or written by someone who has published there, or cited a few hundred times. Preprints that later get accepted, and authors who later publish at those venues, are picked up by the weekly run. 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.