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.

12 papers of 11,817Sort Recent · Most cited
  1. 2025
    DS-TPU: Dynamical System for on-Device Lifelong Graph Learning with Nonlinear Node InteractionChunshu Wu, Ruibing Song, Chuan Liu … T. GengInternational Symposium on Computer Architecture
  2. 2024
    Retrospective Feature Estimation for Continual LearningNghia Nguyen, Hieu Trung Nguyen, Ang Li … Khoa D. DoanTrans. Mach. Learn. Res.
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  3. 2023PDF ↗
  4. 2023PDF ↗
  5. 2022
    Information-theoretic Online Memory Selection for Continual LearningShengyang Sun, Daniele Calandriello, Huiyi Hu … Michalis K. TitsiasICLR
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  6. 2021
    One Pass ImageNetHuiyi Hu, Ang Li, Daniele Calandriello, Dilan GörürarXiv
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  7. 2021
    Task-agnostic Continual Learning with Hybrid Probabilistic ModelsPolina Kirichenko, Mehrdad Farajtabar, Dushyant Rao … Razvan PascanuarXiv
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  8. 2020
  9. 2020
    The Effectiveness of Memory Replay in Large Scale Continual LearningYogesh Balaji, Mehrdad Farajtabar, Dong Yin … Ang LiarXiv
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  10. 2020
    SOLA: Continual Learning with Second-Order Loss ApproximationDong Yin, Mehrdad Farajtabar, Ang Li … A. MottarXiv
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  11. 2020
    Learning to Incentivize Other Learning AgentsJiachen Yang, Ang Li, Mehrdad Farajtabar … Hongyuan ZhaNeurIPS · Georgia Institute of Technology · Google (United States)
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  12. 2019
    Orthogonal Gradient Descent for Continual LearningMehrdad Farajtabar, Navid Azizan, A. Mott, Ang LiAISTATS · Google (United States)
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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.