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.

6 papers of 8,653Sort Recent · Most cited
  1. 2020
    Pseudo-Rehearsal: Achieving Deep Reinforcement Learning without Catastrophic ForgettingCraig Atkinson, Brendan McCane, Lech Szymanski, Anthony RobinsNeurocomputing · University of Otago
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  2. 2020
    Motivational engine and long-term memory coupling within a cognitive architecture for lifelong open-ended learningJ. A. Becerra, Alejandro Romero, Francisco Bellas, Richard J. DuroNeurocomputing · Universidade da Coruña
  3. 2020PDF ↗
  4. 2020
    Autonomous cognition development with lifelong learning: A self-organizing and reflecting cognitive networkKe Huang, Xin Ma, Rui Song … Yibin LiNeurocomputing · Shandong University
  5. 2020
    Prevention of catastrophic interference and imposing active forgetting with generative methodsSergey Sukhov, Mikhail Leontev, Alexander Miheev, Kirill SviatovNeurocomputing · Kotelnikov Institute of Radioengineering and Electronics of the Russian Academy of Sciences · Ulyanovsk State University · +1
  6. 2020
    Efficient Continual Learning in Neural Networks with Embedding RegularizationJary Pomponi, Simone Scardapane, Vincenzo Lomonaco, Aurelio UnciniNeurocomputing · Sapienza University of Rome · University of Bologna
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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.