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.

12 papers of 11,817Sort Recent · Most cited
  1. 2019
    Continual Learning with Adaptive Weights (CLAW)Tameem Adel, Han Zhao, Richard E. TurnerICLR
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  2. 2019
    The Implicit Bias of Depth: How Incremental Learning Drives GeneralizationDaniel Gissin, Shai Shalev‐Shwartz, Amit DanielyICLR · Hebrew University of Jerusalem
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  3. 2019
    Compositional Language Continual LearningYuanpeng Li, Liang Zhao, Kenneth Ward Church, Mohamed ElhoseinyICLR
  4. 2019
    LAMOL: LAnguage MOdeling for Lifelong Language LearningFan-Keng Sun, Cheng-Hao Ho, Hung-yi LeeICLR · Massachusetts Institute of Technology · National Taiwan University
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  5. 2019
    Uncertainty-guided Continual Learning with Bayesian Neural NetworksSayna Ebrahimi, Mohamed Elhoseiny, Trevor Darrell, Marcus RohrbachICLR · University of California, Berkeley · King Abdullah University of Science and Technology · +1
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  6. 2019
    Continual learning with hypernetworksJohannes von Oswald, Christian Henning, J. Sacramento, B. GreweICLR
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  7. 2019
    Adversarially robust transfer learningAli Shafahi, Parsa Saadatpanah, Chen Zhu … T. GoldsteinICLR
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  8. 2019
    A comprehensive, application-oriented study of catastrophic forgetting in DNNsBenedikt Pfülb, Alexander GepperthICLR · Fulda University of Applied Sciences
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  9. 2019
    Functional Regularisation for Continual Learning using Gaussian ProcessesMichalis K. Titsias, Jonathan Schwarz, Alexander Matthews … Yee Whye TehICLR · Google (United States)
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  10. 2019
    Progressive Memory Banks for Incremental Domain AdaptationNabiha Asghar, Lili Mou, Kira A. Selby … Xin JiangICLR
  11. 2019
  12. 2019
    Deep Online Learning via Meta-Learning: Continual Adaptation for Model-Based RLAnusha Nagabandi, Chelsea Finn, Sergey LevineICLR · University of California, Berkeley
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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. 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.