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.

5 papers of 11,817Sort Recent · Most cited
  1. 2019
    Adaptive face tracking based on online learningAasim Khurshid, Jacob ScharcanskiAnais Estendidos do XXXII Conference on Graphics, Pattern… · Universidade Federal do Rio Grande do Sul
  2. 2018
    Adaptive Incremental Gaussian Mixture Network for Non-Stationary Data Stream ClassificationJorge C. Chamby-Diaz, Mariana Recamonde‐Mendoza, Ana L. C. Bazzan, Ricardo GrunitzkiIJCNN · Universidade Federal do Rio Grande do Sul
  3. 2015
    A Fast Incremental Gaussian Mixture ModelRafael Pinto, Paulo Martins EngelPLOS · Universidade Federal do Rio Grande do Sul
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  4. 2012
    Using a Gaussian mixture neural network for incremental learning and roboticsMilton Roberto Heinen, Paulo Martins Engel, Rafael PintoIJCNN · Universidade do Estado de Santa Catarina · Universidade Federal do Rio Grande do Sul
  5. 2011
    IGMN: An incremental connectionist approach for concept formation, reinforcement learning and roboticsMilton Roberto Heinen, Paulo Martins EngelJournal of Applied Computing Research · Universidade Federal do Rio Grande do Sul
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.