Related work

The foundational work on continual learning, 1988 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

5 papers of 6,984Sort Recent · Most cited
  1. 2024
    Wake-Sleep Energy Based Models for Continual LearningVaibhav Singh, Anna Choromanska, Shuang Li, Yilun DuCVPR · Centre Universitaire de Mila · New York University · +2
  2. 2023
    Latent Space Evolution under Incremental Learning with Concept Drift (Student Abstract)Charles Bourbeau, Audrey DurandAAAI · Université Laval · Centre Universitaire de Mila
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  3. 2022
    Towards Continual Reinforcement Learning: A Review and PerspectivesKhimya Khetarpal, Matthew Riemer, Irina Rish, Doina PrecupJournal of Artificial Intelligence Research · Google DeepMind (United Kingdom) · McGill University · +2
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  4. 2021
    Understanding Capacity Saturation in Incremental LearningShenyang Huang, Vincent François-Lavet, Guillaume RabusseauCanadian Conference on Artificial Intelligence · Centre Universitaire de Mila · McGill University · +3
  5. 2021
    Continuous Coordination As a Realistic Scenario for Lifelong LearningHadi Nekoei, Akilesh Badrinaaraayanan, Aaron Courville, Sarath ChandarICML · Centre Universitaire de Mila · Université de Montréal · +1
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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 lists only papers we have a reason to trust: published at a venue like NeurIPS, ICML, ICLR, CVPR or TPAMI, or led by someone who has published there, or cited a few hundred times. Preprints that later get accepted, and authors who later publish at those venues, are picked up by the weekly run. 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.