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.

8 papers of 8,653Sort Recent · Most cited
  1. 2020
    Simple Lifelong Learning MachinesJoshua T. Vogelstein, Jayanta Dey, Hayden S. Helm … Carey E. PriebeTPAMI · Johns Hopkins University · Baylor College of Medicine · +1
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  2. 2022
    Three types of incremental learningGido M. van de Ven, Tinne Tuytelaars, Andreas S. ToliasNature Machine Intelligence · Baylor College of Medicine · University of Cambridge · +2
  3. 2022
    Biological underpinnings for lifelong learning machinesDhireesha Kudithipudi, Mario Aguilar-Simon, Jonathan Babb … Hava T. SiegelmannNature Machine Intelligence · The University of Texas at San Antonio · Intelligent Systems Research (United States) · +24
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  4. 2021
    Omnidirectional Transfer for Quasilinear Lifelong LearningJayanta Dey, Joshua T Vogelstein, Hayden S. Helm … Carey E. PriebeResearch Square · Johns Hopkins University · Baylor College of Medicine · +1
  5. 2021
    Avalanche: an End-to-End Library for Continual LearningVincenzo Lomonaco, Lorenzo Pellegrini, Andrea Cossu … Davide MaltoniCVPR · University of Pisa · University of Bologna · +12
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  6. 2021
    Class-Incremental Learning with Generative ClassifiersGido M. van de Ven, Zhe Li, Andreas S. ToliasCVPR · Baylor College of Medicine
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  7. 2020
    Brain-inspired replay for continual learning with artificial neural networksGido M. van de Ven, Hava T. Siegelmann, Andreas S. ToliasNature Communications · Baylor College of Medicine · University of Cambridge · +3
  8. 2018
    Generative replay with feedback connections as a general strategy for continual learningGido M. van de Ven, Andreas S. ToliasarXiv · Baylor College of Medicine
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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.