Related work

The foundational work on continual learning, 1959 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

5 papers of 11,817Sort Recent · Most cited
  1. 2019
    Online Gaussian Process State-space Model: Learning and Planning for Partially Observable Dynamical SystemsSoon-Seo Park, Young-Jin Park, Young-Jae Min, Han‐Lim ChoiInternational Journal of Control Automation and Systems · NCSOFT (South Korea) · NAVER Cloud (South Korea) · +4
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  2. 2019
    Motion Planning Networks: Bridging the Gap Between Learning-Based and Classical Motion PlannersAhmed H. Qureshi, Yinglong Miao, Anthony Simeonov, Michael C. YipIEEE Transactions · University of California San Diego · Massachusetts Institute of Technology
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  3. 2019
    LAMOL: LAnguage MOdeling for Lifelong Language LearningFan-Keng Sun, Cheng-Hao Ho, Hung-yi LeeICLR · Massachusetts Institute of Technology · National Taiwan University
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  4. 2019
    Autoencoder-Based Incremental Class Learning without Retraining on Old DataEuntae Choi, Kyungmi Lee, Ki‐Young ChoiarXiv · Seoul National University · Massachusetts Institute of Technology
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  5. 2019
    Continual Learning with Self-Organizing MapsPouya Bashivan, Martin Schrimpf, Robert Ajemian … Yuhai TuarXiv · Massachusetts Institute of Technology
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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.