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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.

11 papers of 11,817Sort Recent · Most cited
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  1. 2001
    Reducing computations in incremental learning for feedforward neural network with long-term memoryM. Kobyashi, Abu Sarwar Zamani, Seiichi Ozawa, Shigeo AbeIJCNN · Kobe University
  2. 2001
    A clustering approach to incremental learning for feedforward neural networksAndries P. Engelbrecht, R. BritsIJCNN · University of Pretoria
  3. 2001
    Incremental Learning with Respect to New Incoming Input AttributesSheng-Uei Guan, Shanchun LiNeural Processing Letters · National University of Singapore
  4. 2001
    Naive Bayes 문서 분류기를 위한 점진적 학습 모델 연구 ( A Study Incremental Learning Model for Naive Bayes Text Classifier )제욱 김, 한중 김, 상국 이Preprint
  5. 2001
    Life-long learning Cell Structures--continuously learning without catastrophic interferenceFred H. HamkerNeural Networks · California Institute of Technology
  6. 2001
    Incremental Self-Growing Neural Networks with the Changing EnvironmentLijuan Su, Suijing Guan, Yue Heng YeoJournal of Intelligent Systems
  7. 2001
    Learn++: an incremental learning algorithm for supervised neural networksRobi Polikar, L. Upda, S.S. Upda, Vasant HonavarIEEE Transactions · Rowan University · Iowa State University · +1
  8. 2001
    An investigation into catastrophic interference on a SOM networkA. Indrayanto, N.M. AllinsonAdvances in Self-Organising Maps · University of Manchester
  9. 2001
    Synfire chains and catastrophic interferenceJ. Sougné, R. FrenchPreprint
  10. 2001
    Learning Action-Value Functions Using Neural Networks with Incremental Learning AbilityN. ShiragaPreprint
  11. 2001
    Incremental learning with neural networks, evolutionary computation and reinforcement learning algorithmsS. ChalupPreprint
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.