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.

9 papers of 11,817Sort Recent · Most cited
  1. 2026
  2. 2025PDF ↗
  3. 2025PDF ↗
  4. 2025
    Ten Challenging Problems in Federated Foundation ModelsTao Fan, Hanlin Gu, Xuemei Cao … Qiang YangTKDE
    PDF ↗
  5. 2024PDF ↗
  6. 2024PDF ↗
  7. 2022
    FedTracker: Furnishing Ownership Verification and Traceability for Federated Learning ModelShuo Shao, Wenyuan Yang, Hanlin Gu … Kui RenIEEE Transactions · Zhejiang University · Sun Yat-sen University · +3
    PDF ↗
  8. 2024
  9. 2019
    AutoML @ NeurIPS 2018 challenge: Design and ResultsHugo Jair Escalante, Wei-Wei Tu, Isabelle Guyon … Qiang YangMachine Learning · Gleason (United States) · National Institute of Astrophysics, Optics and Electronics · +8
    PDF ↗
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.