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. 2018PDF ↗
  2. 2018
    Continual Classification Learning Using Generative ModelsFrantzeska Lavda, Jason Ramapuram, Magda Gregorová, Alexandros KalousisNeurIPS
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  3. 2018
    Life-Long Disentangled Representation Learning with Cross-Domain Latent HomologiesAlessandro Achille, Tom Eccles, Löıc Matthey … Irina HigginsNeurIPS
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  4. 2018
    Reinforced Continual LearningJu Xu, Zhanxing ZhuNeurIPS
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  5. 2018
    Distributed Weight Consolidation: A Brain Segmentation Case StudyPatrick McClure, Charles Zheng, Jakub Kaczmarzyk … Francisco PereiraNeurIPS · National Institutes of Health · Massachusetts Institute of Technology
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  6. 2018
    Online Structured Laplace Approximations For Overcoming Catastrophic ForgettingHippolyt Ritter, Aleksandar Botev, David BarberNeurIPS
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  7. 2018
    HOUDINI: Lifelong Learning as Program SynthesisLazar Valkov, Dipak Chaudhari, Akash Srivastava … Swarat ChaudhuriNeurIPS · Indian Institute of Technology Bombay · IBM (United States) · +2
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  8. 2018
    Task Agnostic Continual Learning Using Online Variational BayesChen Zeno, Itay Golan, Elad Hoffer, Daniel SoudryNeurIPS
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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.