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.

11 papers of 11,817Sort Recent · Most cited
  1. 2022
    A model of autonomous interactions between hippocampus and neocortex driving sleep-dependent memory consolidationDhairyya Singh, Kenneth A. Norman, Anna C. SchapiroPNAS · University of Pennsylvania · Princeton University
  2. 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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  3. 2022
    Interplay between rule learning and rule switching in a perceptual categorization taskFlora Bouchacourt, S. Tafazoli, Marcelo G. Mattar … Nathaniel D. DawbioRxiv · Princeton University · University of California San Diego
  4. 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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  5. 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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  6. 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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  7. 2019
    Visualizing the PHATE of Neural NetworksScott Gigante, Adam S. Charles, Smita Krishnaswamy, Gal MishneNeurIPS · Yale University · Princeton University · +1
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  8. 2019
    Online Meta-LearningChelsea Finn, Aravind Rajeswaran, Sham M. Kakade, Sergey LevineICML · Stanford University · University of Washington · +3
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  9. 2017
    Reminders of past choices bias decisions for reward in humansAaron M. Bornstein, Mel Win Khaw, Daphna Shohamy, Nathaniel D. DawNature Communications · Princeton University · Columbia University · +2
  10. 2017
    Continual Learning in Generative Adversarial NetsAri Seff, Alex Beatson, Daniel Suo, Han LiuarXiv · Princeton University
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  11. 2004
    Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object CategoriesLi Fei-Fei, Rob Fergus, Pietro PeronaComputer Vision and Image Understanding · Princeton University · University of Oxford · +1
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.