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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.

10 papers of 11,817Sort Recent · Most cited
1999 Clear all
  1. 1999
    Pattern classification by an incremental learning fuzzy neural networkGary G. Yen, Phayung MeesadIJCNN · Oklahoma State University
  2. 1999
    An Incremental Learning Neural Network for Pattern ClassificationCHENG-AN HUNG, Sheng‐Fuu LinInternational Journal of Pattern Recognition and Artifici… · National Yang Ming Chiao Tung University
  3. 1999
    Handling concept drifts in incremental learning with support vector machinesNadeem Ahmed Syed, Huan Liu, Kah Kay SungKDD · National University of Singapore
  4. 1999
    Incremental Learning with a Modular-Type Neural NetworkSeiji Ishihara, T. NaganoPreprint
  5. 1999
    Catastrophic forgetting in connectionist networks.Robert M. French, R FrenchTrends in Cognitive Sciences · University of Liège
  6. 1999
    Incremental learning for Bayesian classification of imagesAditya Vailaya, Anil K. JainICIP · Michigan State University
  7. 1999
    Catastrophic forgetting in simple networks: an analysis of the pseudorehearsal solution.Marcus Frean, Anthony RobinsNetwork Computation in Neural Systems · Victoria University of Wellington · University of Otago
  8. 1999
    A Modular-Type Neural Network with RBF Output Units.Seiji Ishihara, Takashi NaganoThe Brain & Neural Networks · Hosei University
  9. 1999
    Catastrophic Forgetting in Connectionist Networks: Causes, Consequences and SolutionsR. FrenchPreprint
  10. 1999
    On-line pattern classification using a neural-network-based incremental learning systemC. Lim, H. H. Toh, T. S. LeePreprint
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.