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.

4 papers of 11,817Sort Recent · Most cited
  1. 2021
    Rethinking Architecture Design for Tackling Data Heterogeneity in Federated LearningLiangqiong Qu, Yuyin Zhou, Paul Pu Liang … Daniel L. RubinCVPR · Stanford University · University of California, Santa Cruz · +2
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  2. 2021
    Avoiding Catastrophe: Active Dendrites Enable Multi-Task Learning in Dynamic EnvironmentsAbhiram Iyer, Karan Grewal, Akash Velu … Subutai AhmadFrontiers · Carnegie Mellon University · Stanford University · +1
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  3. 2021
    From partners to populations: A hierarchical Bayesian account of coordination and conventionRobert D. Hawkins, Michael Franke, Michael C. Frank … Noah D. GoodmanPsychological Review · Princeton University · Osnabrück University · +3
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  4. 2021
    DyStaB: Unsupervised Object Segmentation via Dynamic-Static Bootstrapping*Yanchao Yang, Brian Lai, Stefano SoattoCVPR · Stanford 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.