Related work

The foundational work on continual learning, 1980 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

59 papers of 8,653 · showing 51–59Sort Recent · Most cited
  1. 2020
    Learning to Continually LearnBeaulieu Shawn, Frati Lapo, Miconi Thomas … Cheney NickFrontiers
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  2. 2019
    A Spike Time-Dependent Online Learning Algorithm Derived From Biological OlfactionAyon Borthakur, Thomas A. ClelandFrontiers · Cornell University
  3. 2018
    Lifelong Learning of Spatiotemporal Representations With Dual-Memory Recurrent Self-OrganizationGerman I. Parisi, Jun Tani, Cornelius Weber, Stefan WermterFrontiers · Universität Hamburg · Okinawa Institute of Science and Technology Graduate University
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  4. 2016
    Towards Lifelong Object Learning by Integrating Situated Robot Perception and Semantic Web MiningYoung Jay, Valerio Basile, Kunze Lars … Nick HawesFrontiers · University of Birmingham · Laboratoire d'Informatique, Signaux et Systèmes de Sophia Antipolis
  5. 2016
    One-Class to Multi-Class Model Update Using the Class-Incremental Optimum-Path Forest ClassifierMateus Riva, Moacir Antonelli Ponti, de Campos TeofiloFrontiers · Universidade de São Paulo · University of Surrey
  6. 2013
    The stability-plasticity dilemma: investigating the continuum from catastrophic forgetting to age-limited learning effectsMartial Mermillod, Aurélia Bugaïska, Patrick BoninFrontiers · Centre National de la Recherche Scientifique · Institut Universitaire de France · +3
  7. 2013
    Sleep-Dependent Synaptic Down-Selection (II): Single-Neuron Level Benefits for Matching, Selectivity, and SpecificityAtif Hashmi, Andrew Nere, Giulio TononiFrontiers · University of Wisconsin–Madison
  8. 2013
    Incremental learning of skill collections based on intrinsic motivationJan Hendrik Metzen, Frank KirchnerFrontiers · University of Bremen · German Research Centre for Artificial Intelligence
  9. 2009
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.