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.

8 papers of 8,653Sort Recent · Most cited
  1. 2025
    A theory of initialisation’s impact on specialisationDevon Jarvis, Sebastian Lee, Clémentine Carla Juliette Dominé … Stefano Sarao MannelliICLR · University of the Witwatersrand · Simons Foundation · +6
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  2. 2022
    Exact learning dynamics of deep linear networks with prior knowledgeClémentine Dominé, Lukas Braun, James E. Fitzgerald, Andrew SaxeNeurIPS · Gatsby Computational Neuroscience Unit · University College London · +4
  3. 2023
    Continual task learning in natural and artificial agentsTimo Flesch, Andrew Saxe, Christopher SummerfieldTrends in Neurosciences · University of Oxford · Sainsbury Laboratory · +3
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  4. 2023
    Modelling continual learning in humans with Hebbian context gating and exponentially decaying task signalsTimo Flesch, Dávid Nagy, Andrew Saxe, Christopher SummerfieldPLOS · University of Oxford · HUN-REN Wigner Research Centre for Physics · +5
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  5. 2022
    An analytical theory of curriculum learning in teacher–student networksLuca Saglietti, Stefano Sarao Mannelli, Andrew SaxeNeurIPS · Bocconi University · Gatsby Computational Neuroscience Unit · +1
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  6. 2022
    Maslow's Hammer for Catastrophic Forgetting: Node Re-Use vs Node ActivationSebastian Lee, Stefano Sarao Mannelli, Claudia Clopath … Andrew SaxearXiv
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  7. 2021
    Continual Learning in the Teacher-Student Setup: Impact of Task SimilaritySebastian Lee, Sebastian Goldt, Andrew SaxeICML · Microsoft Research (United Kingdom) · Scuola Internazionale Superiore di Studi Avanzati · +1
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  8. 2016
    Active Long Term Memory NetworksTommaso Furlanello, Jiaping Zhao, Andrew Saxe … Bosco S. TjanarXiv
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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.