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.

9 papers of 8,653Sort Recent · Most cited
  1. 2024
    Statistical Context Detection for Deep Lifelong Reinforcement LearningJeffery Dick, Saptarshi Nath, Christos Peridis … Andrea SoltoggioCoLLAs
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  2. 2024
    A collective AI via lifelong learning and sharing at the edgeAndrea Soltoggio, Eseoghene Ben-Iwhiwhu, Vladimir Braverman … Soheil KolouriNature Machine Intelligence · Loughborough University · Rice University · +21
  3. 2023
    Sharing Lifelong Reinforcement Learning Knowledge via Modulating MasksSaptarshi Nath, Christos Peridis, Eseoghene Ben-Iwhiwhu … Andrea SoltoggioCoLLAs
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  4. 2023PDF ↗
  5. 2023
    A Domain-Agnostic Approach for Characterization of Lifelong Learning SystemsMegan M. Baker, Alexander New, Mario Aguilar-Simon … Gautam K. VallabhaNeural Networks · Johns Hopkins University Applied Physics Laboratory · Teledyne Technologies (United States) · +13
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  6. 2023
    Lifelong Reinforcement Learning with Modulating MasksEseoghene Ben-Iwhiwhu, Saptarshi Nath, Praveen K. Pilly … Andrea SoltoggioTMLR
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  7. 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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  8. 2020
    Detecting Changes and Avoiding Catastrophic Forgetting in Dynamic Partially Observable EnvironmentsJeffery Dick, Paweł Ładosz, Eseoghene Ben-Iwhiwhu … Andrea SoltoggioFrontiers · Loughborough University · Teikyo University · +1
  9. 2018
    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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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. By default it shows the papers we have a reason to trust: published at a venue like NeurIPS, ICML, ICLR, CVPR or TPAMI, or written by someone who has published there, or cited a few hundred times. The rest are one click away under “All papers”. 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.