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. 2021
    Kernel Continual LearningMahammad Mahdi Derakhshani, Xiantong Zhen, Ling Shao, Cees G. M. SnoekICML · University of Amsterdam · Inception Institute of Artificial Intelligence
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  2. 2021
    Continual Learning in the Teacher-Student Setup: Impact of Task SimilaritySebastian Lee, Sebastian Goldt, Andrew SaxeICML · Microsoft Research (United Kingdom) · Scuola Internazionale Superiore di Studi Avanzati · +1
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  3. 2021
    Continuous Coordination As a Realistic Scenario for Lifelong LearningHadi Nekoei, Akilesh Badrinaaraayanan, Aaron Courville, Sarath ChandarICML · Centre Universitaire de Mila · Université de Montréal · +1
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  4. 2021
    GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental LearningIdan Achituve, Aviv Navon, Yochai Yemini … Ethan FetayaICML · Bar-Ilan University
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  5. 2021
    Online Limited Memory Neural-Linear Bandits with Likelihood MatchingOfir Nabati, Tom Zahavy, Shie MannorICML · Technion – Israel Institute of Technology · Google DeepMind (United Kingdom)
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  6. 2021
  7. 2021
    Variational Auto-Regressive Gaussian Processes for Continual LearningSanyam Kapoor, Theofanis Karaletsos, Thang D. BuiICML · Supélec · University of Applied Sciences and Arts of Southern Switzerland · +3
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  8. 2021
    Addressing Catastrophic Forgetting in Few-Shot ProblemsPauching Yap, Hippolyt Ritter, David BarberICML · University College London
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  9. 2021
    Federated Continual Learning with Weighted Inter-client TransferJaehong Yoon, Wonyong Jeong, Giwoong Lee … Sung Ju HwangICML · Korea Advanced Institute of Science and Technology · Korea Institute of Science and Technology
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  10. 2021
    Overcoming Catastrophic Forgetting by Bayesian Generative RegularizationPatrick H. Chen, Wei Wei, Cho‐Jui Hsieh, Bo DaiICML · University of California, Los Angeles
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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.