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. 2026
    Dimensionality Controls When Modularity Helps in Continual LearningKathrin Korte, C. Adriano, Joachim Winther Pedersen … Sebastian RisiarXiv
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  2. 2026PDF ↗
  3. 2025
    When Does Neuroevolution Outcompete Reinforcement Learning in Transfer Learning Tasks?Eleni Nisioti, Erwan Plantec, Milton L. Montero … Sebastian RisiGenetic and Evolutionary Computation Conference · IT University of Copenhagen
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  4. 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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  5. 2020
    Meta-Learning through Hebbian Plasticity in Random NetworksElias Najarro, Sebastian RisiNeurIPS · IT University of Copenhagen
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  6. 2019
    Towards continual reinforcement learning through evolutionary meta-learningDjordje Grbic, Sebastian RisiGenetic and Evolutionary Computation Conference Companion · IT University of Copenhagen
  7. 2017
    Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural NetworksAndrea Soltoggio, Kenneth O. Stanley, Sebastian RisiNeural Networks · Loughborough University · University of Central Florida · +1
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  8. 2017
    Continual and One-Shot Learning Through Neural Networks with Dynamic External MemoryBenno Lüders, Mikkel Schläger, Aleksandra Korach, Sebastian RisiSpringer LNCS · IT University of Copenhagen
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.