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.

5 papers of 8,653Sort Recent · Most cited
  1. 2019
    Incremental Learning Using a Grow-and-Prune Paradigm With Efficient Neural NetworksXiaoliang Dai, Hongxu Yin, Niraj K. JhaIEEE Transactions · Princeton University
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  2. 2019
    Dreaming to Distill: Data-Free Knowledge Transfer via DeepInversionHongxu Yin, Pavlo Molchanov, Jose M. Álvarez … Jan KautzCVPR · Princeton University · University of Illinois Urbana-Champaign
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  3. 2019
    Continual Adaptation for Efficient Machine CommunicationRobert D. Hawkins, Minae Kwon, Dorsa Sadigh, Noah D. GoodmanCoNLL · Princeton University · Department of Physics, Mathematics and Informatics · +1
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  4. 2019
    Visualizing the PHATE of Neural NetworksScott Gigante, Adam S. Charles, Smita Krishnaswamy, Gal MishneNeurIPS · Yale University · Princeton University · +1
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  5. 2019
    Online Meta-LearningChelsea Finn, Aravind Rajeswaran, Sham M. Kakade, Sergey LevineICML · Stanford University · University of Washington · +3
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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.