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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.

5 papers of 8,653Sort Recent · Most cited
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  1. 2015
    Sequential Covariance-Matrix Estimation with Application to Mitigating Catastrophic ForgettingTomer Lancewicki, Benjamin Goodrich, Itamar ArelICML · University of Tennessee at Knoxville
  2. 2014
    Neuron clustering for mitigating catastrophic forgetting in feedforward neural networksBen Goodrich, Itamar ArelIEEE Symposium on Computational Intelligence in Dynamic a… · University of Tennessee at Knoxville
  3. 2014
    Unsupervised neuron selection for mitigating catastrophic forgetting in neural networksBen Goodrich, Itamar ArelIEEE 57th International Midwest Symposium on Circuits and… · University of Tennessee at Knoxville
  4. 2013
    Mitigation of catastrophic forgetting in recurrent neural networks using a Fixed Expansion LayerRobert Coop, Itamar ArelIJCNN · University of Tennessee at Knoxville
  5. 2012
    Mitigation of catastrophic interference in neural networks using a fixed expansion layerRobert Coop, Itamar ArelIEEE 55th International Midwest Symposium on Circuits and… · University of Tennessee at Knoxville
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.