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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.

10 papers of 8,653Sort Recent · Most cited
2005 All papers Clear all
  1. 2005
    Evolving improved incremental learning schemes for neural network systemsTebogo Seipone, John A. BullinariaIEEE Congress on Evolutionary Computation · University of Birmingham
  2. 2005
    EVOLVING NEURAL NETWORKS THAT SUFFER MINIMAL CATASTROPHIC FORGETTINGTebogo Seipone, John A. BullinariaModeling Language, Cognition and Action · University of Birmingham
  3. 2005
    Hebbian learning rule restraining catastrophic forgetting in pulse neural networkMakoto Motoki, Tomoki Hamagami, Seiichi Koakutsu, Hironori HirataElectrical Engineering in Japan · Chiba University
  4. 2005
    Speaker Identification Based on Incremental Learning Neural NetworkKwang-Seung Heo, Kwee-Bo SimInternational Journal of Fuzzy Logic and Intelligent Systems
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  5. 2005
    Incremental learning in hierarchical neural networks for object recognitionRebecca Fay, Friedhelm Schwenker, Günther PalmSecond International Conference on Informatics in Control… · Universität Ulm
  6. 2005
    The Evolution of Minimal Catastrophic Forgetting in Neural SystemsJ. Bullinaria, T. SeiponePreprint
  7. 2005
    Toward a Formal Framework for Continual LearningMark B. RingPreprint
  8. 2005
    A biologically plausible strategy for the avoidance of catastrophic interferenceJ. AddisonPreprint
  9. 2005
    Hebbian networks for averting the problem of catastrophic interferenceJ. E. IvancichPreprint
  10. 2005
    Incremental Learning with the Neural Network TreesT. Takeda, Qiangfu Zhao, Yong LiuNeural Parallel Sci. Comput.
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.