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.

5 papers of 8,653Sort Recent · Most cited
  1. 2021
    Continual Density Ratio Estimation in an Online SettingYu Chen, Song Liu, Tom Diethe, Peter FlacharXiv
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  2. 2020PDF ↗
  3. 2020
    Optimal Continual Learning has Perfect Memory and is NP-hardJeremias Knoblauch, Hisham Husain, Tom DietheICML · University of Warwick · The Alan Turing Institute · +2
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  4. 2019
    Facilitating Bayesian Continual Learning by Natural Gradients and Stein GradientsYu Chen, Tom Diethe, Neil D. LawrencearXiv · University of Bristol · Amazon (Germany)
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  5. 2019
    Continual Learning in PracticeTom Diethe, Tom Borchert, Eno Thereska … Neil D. LawrenceNeurIPS · Amazon (Germany)
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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.