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.

9 papers of 11,817Sort Recent · Most cited
  1. 2024
    Sequential Learning in the Dense Associative MemoryHayden McAlister, Anthony Robins, Lech SzymanskiNeural Computation
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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. 2018
    Pseudo-Rehearsal: Achieving Deep Reinforcement Learning without Catastrophic ForgettingCraig Atkinson, Brendan McCane, Lech Szymanski, Anthony RobinsNeurocomputing · University of Otago
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  4. 2018
    Pseudo-Recursal: Solving the Catastrophic Forgetting Problem in Deep Neural NetworksCraig Atkinson, Brendan McCane, Lech Szymanski, Anthony RobinsarXiv
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  5. 1993
    Catastrophic forgetting in neural networks: the role of rehearsal mechanismsAnthony RobinsProceedings 1993 The First New Zealand International Two-… · University of Otago
  6. 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
  7. 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
  8. 1998
    Catastrophic Forgetting and the Pseudorehearsal Solution in Hopfield-type NetworksAnthony Robins, Simon McCallumConnection Science · University of Otago
  9. 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 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.