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.

221 papers of 8,653 · showing 201–221Sort Recent · Most cited
  1. 2019
    Probabilistic Program NeurogenesisCharles E. Martin, Praveen K. PillyThe 2019 Conference on Artificial Life · HRL Laboratories (United States)
  2. 2019
    Overcoming Catastrophic Interference with Bayesian Learning and Stochastic Langevin DynamicsMikhail Leontev, Alexander Mikheev, Kirill Sviatov, Sergey SukhovSpringer LNCS · Ulyanovsk State University · Kotelnikov Institute of Radioengineering and Electronics of the Russian Academy of Sciences · +1
  3. 2019
    Lifelong Learning Starting From ZeroClaes Strannegård, Herman Carlström, Niklas Engsner … Morteza Haghir ChehreghaniSpringer LNCS · Chalmers University of Technology
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  4. 2019
    Automatically inferring task context for continual learningJasmine Collins, Kelvin Xu, Bruno A. Olshausen, Brian CheungConference on Cognitive Computational Neuroscience · University of California, Berkeley
  5. 2019
    Central-Diffused Instance Generation Method in Class Incremental LearningMing-Yu Liu, Yijie WangSpringer LNCS · National University of Defense Technology
  6. 2019
  7. 2019
    Uncertainty-Guided Continual Learning in Bayesian Neural Networks - Extended AbstractSayna Ebrahimi, Mohamed Elhoseiny, Trevor Darrell, Marcus RohrbachCVPR
  8. 2019
  9. 2019
  10. 2019
  11. 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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  12. 2019
    Reconciling meta-learning and continual learning with online mixtures of tasksGhassen Jerfel, Erin Grant, T. Griffiths, K. HellerNeurIPS
  13. 2019
    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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  14. 2019
    Efficient Lifelong Learning with A-GEMArslan Chaudhry, Marc’Aurelio Ranzato, Marcus Rohrbach, Mohamed ElhoseinyICLR
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  15. 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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  16. 2019
    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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  17. 2019
  18. 2019
    Perception Coordination Network: A Neuro Framework for Multimodal Concept Acquisition and BindingYoulu Xing, Xiaofeng Shi, Furao Shen … Ah‐Hwee TanTNNLS · Anhui University · Nanjing University · +2
  19. 2019
    Selfless Sequential LearningRahaf Aljundi, Marcus Rohrbach, Tinne TuytelaarsICLR
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  20. 2019
    Measuring and regularizing networks in function spaceAri S. Benjamin, David Rolnick, Konrad P. KördingICLR · University of Pennsylvania · Philadelphia University
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  21. 2019
    Continual Lifelong Learning with Neural Networks: A ReviewG. I. Parisi, Ronald Kemker, Jose L. Part … Stefan WermterNeural Networks
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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.