Related work

The foundational work on continual learning, 1980 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

6 papers of 8,653Sort Recent · Most cited
  1. 2021
    SPeCiaL: Self-Supervised Pretraining for Continual LearningLucas Caccia, Joëlle PineauSpringer LNCS · McGill University
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  2. 2021
    Block Contextual MDPs for Continual LearningShagun Sodhani, Franziska Meier, Joëlle Pineau, Amy ZhangConference on Learning for Dynamics & Control · Meta (Israel)
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  3. 2021
    New Insights on Reducing Abrupt Representation Change in Online Continual LearningLucas Caccia, Rahaf Aljundi, Nader Asadi … Eugene BelilovskyICLR
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  4. 2020
    Evaluating Logical Generalization in Graph Neural NetworksKoustuv Sinha, Shagun Sodhani, Joëlle Pineau, William L. HamiltonarXiv · McGill University
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  5. 2019
    Online Learned Continual Compression with Stacked Quantization ModuleLucas Caccia, Eugene Belilovsky, M. Caccia, Joëlle PineauarXiv · McGill University · Meta (Israel)
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  6. 2014
    Representation as a ServiceOuais Alsharif, Philip Bachman, Joëlle PineauarXiv
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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.