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
  1. 2018PDF ↗
  2. 2018
    An Empirical Study of Example Forgetting during Deep Neural Network LearningMariya Toneva, Alessandro Sordoni, Rémi Tachet des Combes … Geoffrey J. GordonICLR · Carnegie Mellon University · Microsoft (United States) · +1
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  3. 2018
    Efficient Lifelong Learning with A-GEMArslan Chaudhry, Marc’Aurelio Ranzato, Marcus Rohrbach, Mohamed ElhoseinyICLR
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  4. 2018
    Learning to Learn without Forgetting By Maximizing Transfer and Minimizing InterferenceMatthew Riemer, Ignacio Cases, Robert Ajemian … Gerald TesauroICLR · IBM (United States) · Stanford University · +2
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  5. 2018
  6. 2018
    Selfless Sequential LearningRahaf Aljundi, Marcus Rohrbach, Tinne TuytelaarsICLR
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  7. 2018
    Measuring and regularizing networks in function spaceAri S. Benjamin, David Rolnick, Konrad P. KördingICLR · University of Pennsylvania · Philadelphia University
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  8. 2018
    Memory-based Parameter AdaptationPablo Sprechmann, Siddhant M. Jayakumar, Jack W. Rae … Charles BlundellICLR · Google (United States)
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  9. 2018
    Bayesian Incremental Learning for Deep Neural NetworksMax Kochurov, Timur Garipov, Dmitry Podoprikhin … Dmitry VetrovICLR · Skolkovo Institute of Science and Technology · Samsung (South Korea) · +1
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  10. 2018
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.