Related work

The foundational work on continual learning, 1988 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

59 papers of 6,984 · showing 51–59Sort Recent · Most cited
  1. 2021
    SpaceNet: Make Free Space For Continual LearningGhada Sokar, Decebal Constantin Mocanu, Mykola PechenizkiyNeurocomputing · Eindhoven University of Technology · University of Twente
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  2. 2020
    Pseudo-Rehearsal: Achieving Deep Reinforcement Learning without Catastrophic ForgettingCraig Atkinson, Brendan McCane, Lech Szymanski, Anthony RobinsNeurocomputing · University of Otago
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  3. 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
  4. 2017
    Lifelong Generative ModelingJason Ramapuram, Magda Gregorová, Alexandros KalousisNeurocomputing · University of Geneva · HES-SO University of Applied Sciences and Arts Western Switzerland
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  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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  7. 2008
    Incremental learning of sequence patterns with a modular network modelIchiro Igari, Jun TaniNeurocomputing · RIKEN Center for Brain Science
  8. 2006
    A new ARTMAP-based neural network for incremental learningMu-Chun Su, Jonathan Lee, Kuo-Lung HsiehNeurocomputing · National Central University
  9. 1995
    A neural network architecture for incremental learningShigetoshi Shiotani, Toshio Fukuda, Takanori ShibataNeurocomputing · Nagoya University
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 lists only papers we have a reason to trust: published at a venue like NeurIPS, ICML, ICLR, CVPR or TPAMI, or led by someone who has published there, or cited a few hundred times. Preprints that later get accepted, and authors who later publish at those venues, are picked up by the weekly run. 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.