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.

4 papers of 8,653Sort Recent · Most cited
  1. 2019
    Gated Linear NetworksJoel Veness, Tor Lattimore, David Budden … Marcus HütterAAAI · Google DeepMind (United Kingdom)
    PDF ↗
  2. 2020
    A Combinatorial Perspective on Transfer LearningJianan Wang, Eren Sezener, David Budden … Joel VenessNeurIPS · Google (United States)
    PDF ↗
  3. 2020
    Gaussian Gated Linear NetworksDavid Budden, Adam Marblestone, Eren Sezener … Joel VenessNeurIPS · Google (United States)
    PDF ↗
  4. 2016
    Overcoming catastrophic forgetting in neural networksJames Kirkpatrick, Razvan Pascanu, Neil C. Rabinowitz … Raia HadsellPNAS · Google DeepMind (United Kingdom) · Imperial College London
    PDF ↗
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.