Related work

The foundational work on continual learning, 1988 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

4 papers of 6,984Sort Recent · Most cited
  1. 2013
    Evolving Classifier Ensembles using Dynamic Multi-objective Swarm IntelligenceJean-François Connolly, Éric Granger, Robert SabourinICPR
  2. 2011
    Evolution of heterogeneous ensembles through dynamic particle swarm optimization for video-based face recognitionJean-François Connolly, Éric Granger, Robert SabourinPattern Recognition · Université du Québec à Montréal · École de Technologie Supérieure
  3. 2008
    Supervised Incremental Learning with the Fuzzy ARTMAP Neural NetworkJean-François Connolly, Éric Granger, Robert SabourinSpringer LNCS · École de Technologie Supérieure
  4. 2008
    A comparison of fuzzy ARTMAP and Gaussian ARTMAP neural networks for incremental learningÉric Granger, Jean-François Connolly, Robert SabourinIJCNN · École de Technologie Supérieure
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 lists only papers we have a reason to trust: published at a venue like NeurIPS, ICML, ICLR, CVPR or TPAMI, or led by someone who has published there, or cited a few hundred times. Preprints that later get accepted, and authors who later publish at those venues, are picked up by the weekly run. 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.