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. 2024
    Transformers for Supervised Online Continual LearningJörg Bornschein, Yazhe Li, Amal Rannen-TrikiarXiv
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  2. 2023
    Towards Robust and Efficient Continual Language LearningAdam Fisch, Amal Rannen-Triki, Razvan Pascanu … Marc’Aurelio RanzatoarXiv
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  3. 2024
    Kalman Filter for Online Classification of Non-Stationary DataMichalis K. Titsias, Alexandre Galashov, Amal Rannen-Triki … Jörg BornscheinICLR
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  4. 2023
    NEVIS'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision ResearchJörg Bornschein, Alexandre Galashov, Ross Hemsley … Marc’Aurelio RanzatoJMLR
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  5. 2021
    A study on the plasticity of neural networksTudor Berariu, Wojciech Marian Czarnecki, Soham De … Claudia ClopatharXiv · Imperial College London
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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.