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.

4 papers of 11,817Sort Recent · Most cited
  1. 2021
    Multi-lingual agents through multi-headed neural networksJonathan D. Thomas, Ra ́ul Santos-Rodr ́ıguez, Mihai Anca, Robert J. PiechockiNorthern Lights Deep Learning Workshop · University of Bristol
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  2. 2021
    Convolutional Neural Networks Are Not Invariant to Translation, but They Can Learn to BeValerio Biscione, Jeffrey S. BowersJMLR · University of Bristol
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  3. 2021
    Learning offline: memory replay in biological and artificial reinforcement learningEmma L. Roscow, Raymond Chua, Rui Ponte Costa … Nathan F. LeporaTrends in Neurosciences · Centre de Recerca Matemàtica · McGill University · +2
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  4. 2021
    Continual Learning for Multi-camera RelocalisationAldrich A. Cabrera-Ponce, Manuel Martín-Ortíz, José Martínez-CarranzaSpringer LNCS · Benemérita Universidad Autónoma de Puebla · University of Bristol · +1
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.