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.

11 papers of 11,817Sort Recent · Most cited
  1. 2026
    Continual Learning for Production-Level Machine Learning in Particle AcceleratorsK. Rajput, Alexander Zhukov, Anant Raj … W. BloklandPreprint
  2. 2026
    Continual Learning for Particle AcceleratorsM. Schram, Sen Lin, K. Rajput … W. BloklandPreprint
  3. 2025PDF ↗
  4. 2026
    Rethinking Continual Learning with Progressive Neural CollapseZheng Wang, Wanhao Yu, Li Yang, Sen LinICLR
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  5. 2021
    Continual Learning of Generative Models With Limited Data: From Wasserstein-1 Barycenter to Adaptive CoalescenceMehmet Dedeoğlu, Sen Lin, Zhaofeng Zhang, Junshan ZhangTNNLS · Arizona State University · The Ohio State University · +1
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  6. 2023
    Efficient Self-Supervised Continual Learning with Progressive Task-Correlated Layer FreezingLi Yang, Sen Lin, Fan Zhang … Deliang FanIEEE International Symposium on Quality Electronic Design
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  7. 2023
    Theory on Forgetting and Generalization of Continual LearningSen Lin, Pei-Zhong Ju, Yitao Liang, N. ShroffICML
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  8. 2022
    Beyond Not-Forgetting: Continual Learning with Backward Knowledge TransferSen Lin, Li Yang, Deliang Fan, Junshan ZhangNeurIPS
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  9. 2022
    TRGP: Trust Region Gradient Projection for Continual LearningSen Lin, Li Yang, Deliang Fan, Junshan ZhangICLR
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  10. 2022
    CL-LSG: Continual Learning via Learnable Sparse GrowthLi Yang, Sen Lin, Junshan Zhang, Deliang FanPreprint
  11. 2021
    GROWN: GRow Only When Necessary for Continual LearningYang, Li, Sen Lin, Junshan Zhang, Deliang FanarXiv
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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.