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. 2019
    A Story of Two Streams: Reinforcement Learning Models from Human Behavior and NeuropsychiatryBaihan Lin, Guillermo Cecchi, Djallel Bouneffouf … Irina RishAdaptive Agents and Multi-Agents Systems · Columbia University · IBM (United States) · +1
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  2. 2020
    Improving Performance in Reinforcement Learning by Breaking Generalization in Neural NetworksSina Ghiassian, Banafsheh Rafiee, Yat Long Lo, Adam WhiteAdaptive Agents and Multi-Agents Systems · University of Alberta
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  3. 2019
    Building Knowledge for AI Agents with Reinforcement LearningDoina PrecupAdaptive Agents and Multi-Agents Systems · McGill University
  4. 2018
    Modeling Consecutive Task Learning with Task Graph AgendasDavid Isele, Eric Eaton, Mark Roberts, David W. AhaAdaptive Agents and Multi-Agents Systems · University of Pennsylvania · United States Naval Research Laboratory
  5. 2018
    Incremental Learning of Mental Models for Behavior UnderstandingJan PöppelAdaptive Agents and Multi-Agents Systems · Bielefeld University
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.