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.

21 papers of 8,653Sort Recent · Most cited
  1. 2019
    Lifelong Neural Predictive Coding: Learning Cumulatively Online without ForgettingAlex Ororbia, Ankur Mali, C Lee Giles, Daniel KiferNeurIPS
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  2. 2019
    Random Path Selection for Continual LearningJathushan Rajasegaran, Munawar Hayat, Salman Hameed Khan … Ling ShaoNeurIPS
  3. 2019
    Continual Unsupervised Representation LearningDushyant Rao, Francesco Visin, Andrei Rusu … Raia HadsellNeurIPS · Carnegie Mellon University · Google (United States) · +2
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  4. 2019
    Compacting, Picking and Growing for Unforgetting Continual LearningSteven C. Y. Hung, Cheng-Hao Tu, Cheng‐En Wu … Chu-Song ChenNeurIPS
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  5. 2019
    Visualizing the PHATE of Neural NetworksScott Gigante, Adam S. Charles, Smita Krishnaswamy, Gal MishneNeurIPS · Yale University · Princeton University · +1
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  6. 2019
    Episodic Memory in Lifelong Language LearningCyprien de Masson d’Autume, Sebastian Ruder, Lingpeng Kong, Dani YogatamaNeurIPS
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  7. 2019
    Random Path Selection for Incremental LearningJathushan Rajasegaran, Munawar Hayat, Salman Hameed Khan … Ling ShaoNeurIPS
  8. 2019
    An Adaptive Random Path Selection Approach for Incremental Learning.Jathushan Rajasegaran, Munawar Hayat, Salman Khan … Ming–Hsuan YangNeurIPS
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  9. 2019
    Meta-Learning Representations for Continual LearningKhurram Javed, Martha WhiteNeurIPS · University of Alberta
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  10. 2019
    Uncertainty-based Continual Learning with Adaptive RegularizationHongjoon Ahn, Sungmin Cha, Dong-Gyu Lee, Taesup MoonNeurIPS · Sungkyunkwan University
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  11. 2019
    Improving and Understanding Variational Continual LearningSiddharth Swaroop, Cuong V. Nguyen, Thang D. Bui, Richard E. TurnerNeurIPS
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  12. 2019
    Three scenarios for continual learningGido M. van de Ven, Andreas S. ToliasNeurIPS
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  13. 2019
    Gradient based sample selection for online continual learningRahaf Aljundi, Min Lin, Baptiste Goujaud, Yoshua BengioNeurIPS · KU Leuven · National University of Singapore · +1
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  14. 2019
    Continual Learning in PracticeTom Diethe, Tom Borchert, Eno Thereska … Neil D. LawrenceNeurIPS · Amazon (Germany)
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  15. 2019
    A Unifying Bayesian View of Continual LearningSebastian Farquhar, Yarin GalNeurIPS · University of Oxford
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  16. 2019
    Superposition of Many Models into OneBrian Cheung, A. L. Terekhov, Yubei Chen … Bruno A. OlshausenNeurIPS · University of California, Berkeley
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  17. 2019
  18. 2019
  19. 2019
  20. 2019
    Reconciling meta-learning and continual learning with online mixtures of tasksGhassen Jerfel, Erin Grant, T. Griffiths, K. HellerNeurIPS
  21. 2019
    Experience Replay for Continual LearningDavid Rolnick, Arun Ahuja, Jonathan Schwarz … Greg WayneNeurIPS · California University of Pennsylvania · University of Pennsylvania · +1
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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.