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.

8 papers of 11,817Sort Recent · Most cited
  1. 2025
    MEAL: A Benchmark for Continual Multi-Agent Reinforcement LearningTristan Tomilin, Luka van den Boogaard, Samuel Garcin … Mykola PechenizkiyarXiv
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  2. 2024PDF ↗
  3. 2021
    Avoiding Forgetting and Allowing Forward Transfer in Continual Learning via Sparse NetworksGhada Sokar, Decebal Constantin Mocanu, Mykola PechenizkiySpringer LNCS · Eindhoven University of Technology · University of Twente
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  4. 2023
    COOM: A Game Benchmark for Continual Reinforcement LearningTristan Tomilin, Meng Fang, Yudi Zhang, Mykola PechenizkiyNeurIPS
  5. 2021
    Self-Attention Meta-Learner for Continual LearningGhada Sokar, D. Mocanu, Mykola PechenizkiyAdaptive Agents and Multi-Agent Systems
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  6. 2020
    SpaceNet: Make Free Space For Continual LearningGhada Sokar, Decebal Constantin Mocanu, Mykola PechenizkiyNeurocomputing · Eindhoven University of Technology · University of Twente
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  7. 2020
    Learning Invariant Representation for Continual LearningGhada Sokar, Decebal Constantin Mocanu, Mykola PechenizkiyarXiv · Eindhoven University of Technology · University of Twente
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  8. 2021
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.