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.

8 papers of 8,653Sort Recent · Most cited
  1. 2020
    Detecting Changes and Avoiding Catastrophic Forgetting in Dynamic Partially Observable EnvironmentsJeffery Dick, Paweł Ładosz, Eseoghene Ben-Iwhiwhu … Andrea SoltoggioFrontiers · Loughborough University · Teikyo University · +1
  2. 2020
    Exploring Neuromodulation for Dynamic LearningAnurag Daram, Ángel Yanguas-Gil, Dhireesha KudithipudiFrontiers · The University of Texas at San Antonio · Argonne National Laboratory
  3. 2020
    Emergence of Stable Synaptic Clusters on Dendrites Through Synaptic RewiringThomas Limbacher, Robert LegensteinFrontiers · Graz University of Technology
  4. 2020
  5. 2020
    Bio-Inspired Techniques in a Fully Digital Approach for Lifelong LearningS. Bianchi, Irene Muñoz-Martín, Daniele IelminiFrontiers · Politecnico di Milano
  6. 2020
    Self-Net: Lifelong Learning via Continual Self-ModelingJaya Krishna Mandivarapu, Blake Camp, Rolando EstradaFrontiers · Georgia State University
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  7. 2020PDF ↗
  8. 2020
    Learning to Continually LearnBeaulieu Shawn, Frati Lapo, Miconi Thomas … Cheney NickFrontiers
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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.