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.

13 papers of 8,653Sort Recent · Most cited
  1. 2025
    Humans and neural networks show similar patterns of transfer and interference during continual learningEleanor Holton, Lukas Braun, Jessica A. F. Thompson … Christopher SummerfieldNature Human Behaviour · Princeton University · University of Oxford · +1
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  2. 2025
    Overcoming classic challenges for artificial neural networks by providing incentives and practiceKazuki Irie, Brenden M. LakeNature Machine Intelligence · Harvard University · Princeton University
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  3. 2025
    Optimal protocols for continual learning via statistical physics and control theoryFrancesco Mori, Stefano Sarao Mannelli, Francesca MignaccoICLR · University of Oxford · University of the Witwatersrand · +4
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  4. 2024
    Reconciling shared versus context-specific information in a neural network model of latent causesQihong Lu, Tan T Nguyen, Qiong Zhang … Kenneth A. NormanScientific Reports · Princeton University · Washington University in St. Louis · +3
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  5. 2024
    Blocked training facilitates learning of multiple schemasAndre Beukers, Silvy Collin, Ross P. Kempner … Kenneth A. NormanCommunications Psychology · Princeton University · Tilburg University · +1
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  6. 2023
    Toward a More Neurally Plausible Neural Network Model of Latent Cause InferenceQihong Lu, Tan Tien Nguyen, Uri Hasson … Kenneth A. NormanConference on Cognitive Computational Neuroscience · Princeton University · Washington University in St. Louis · +1
  7. 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
  8. 2020
    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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  9. 2020
    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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  10. 2020
    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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  11. 2019
    Visualizing the PHATE of Neural NetworksScott Gigante, Adam S. Charles, Smita Krishnaswamy, Gal MishneNeurIPS · Yale University · Princeton University · +1
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  12. 2019
    Online Meta-LearningChelsea Finn, Aravind Rajeswaran, Sham M. Kakade, Sergey LevineICML · Stanford University · University of Washington · +3
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  13. 2017
    Continual Learning in Generative Adversarial NetsAri Seff, Alex Beatson, Daniel Suo, Han LiuarXiv · Princeton University
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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. By default it shows the papers we have a reason to trust: published at a venue like NeurIPS, ICML, ICLR, CVPR or TPAMI, or written by someone who has published there, or cited a few hundred times. The rest are one click away under “All papers”. 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.