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.

6 papers of 8,653Sort Recent · Most cited
  1. 2026
    Plateau-gated one-shot plasticity supports continual recognition memoryGuanchun Li, Sandro Romani, Jeffrey C. MageebioRxiv · Howard Hughes Medical Institute · Janelia Research Campus
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  2. 2026
    Structural generalization and continual learning enabled by factorized entorhinal-hippocampal memory and entorhinal-parietal action circuitsJaedong Hwang, Sujaya Neupane, Mehrdad Jazayeri, Ila FietebioRxiv · McGovern Institute for Brain Research · Coherent (United States) · +1
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  3. 2024
    Stability through plasticity: Finding robust memories through representational driftMaanasa Natrajan, James E. FitzgeraldbioRxiv · Northwestern University · Howard Hughes Medical Institute · +3
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  4. 2023
    Exact learning dynamics of deep linear networks with prior knowledgeClémentine Dominé, Lukas Braun, James E. Fitzgerald, Andrew SaxeJournal of Statistical Mechanics Theory and Experiment · Gatsby Computational Neuroscience Unit · University College London · +4
  5. 2022
    Representational drift: Emerging theories for continual learning and experimental future directions.Laura Driscoll, Lea Duncker, Christopher D. HarveyCurrent Opinion in Neurobiology · Stanford University · Howard Hughes Medical Institute · +1
  6. 2019
    Task representations in neural networks trained to perform many cognitive tasksGuangyu Robert Yang, Madhura R. Joglekar, Hui Song … Xiao‐Jing WangNature Neuroscience · New York University · Columbia University · +5
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.