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.

7 papers of 8,653Sort Recent · Most cited
  1. 2026PDF ↗
  2. 2024
    Unsupervised Replay Strategies for Continual Learning with Limited DataAnthony Bazhenov, Pahan Dewasurendra, Giri P. Krishnan, Jean Erik DelanoisIEEE International Joint Conference on Neural Network · University of California San Diego
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  3. 2022
    Sleep-like unsupervised replay reduces catastrophic forgetting in artificial neural networksTimothy Tadros, Giri P. Krishnan, Ramyaa Ramyaa, Maxim BazhenovNature Communications · University of California San Diego · New Mexico Institute of Mining and Technology
  4. 2021
    Replay in Deep Learning: Current Approaches and Missing Biological ElementsTyler L. Hayes, Giri P. Krishnan, Maxim Bazhenov … Christopher KananNeural Computation · Rochester Institute of Technology · University of California San Diego · +5
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  5. 2020
    Author response: Can sleep protect memories from catastrophic forgetting?Oscar C. González, Yury Sokolov, Giri P. Krishnan … Maxim BazhenovPreprint · University of California San Diego
  6. 2020
    Biologically Inspired Sleep Algorithm for Reducing Catastrophic Forgetting in Neural NetworksTimothy Tadros, Giri P. Krishnan, Ramyaa Ramyaa, Maxim BazhenovAAAI · University of California San Diego · New Mexico Institute of Mining and Technology
  7. 2019
    Biologically inspired sleep algorithm for artificial neural networksGiri P. Krishnan, Timothy Tadros, Ramyaa Ramyaa, Maxim BazhenovarXiv
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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.