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.

11 papers of 8,653Sort Recent · Most cited
  1. 2026
    Learning Adaptive and Expandable Mixture Model for Continual LearningFei Ye, yongcheng zhong, Qihe Liu … Shijie ZhouAAAI · University of Electronic Science and Technology of China · University of York
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  2. 2025
    Dynamic Expansion Diffusion Learning for Lifelong Generative ModellingFei Ye, Adrian G. Borş, Kun ZhangAAAI · University of Electronic Science and Technology of China · University of York · +1
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  3. 2025
    Continual Unsupervised Generative Modelling via Online Optimal TransportFei Ye, Adrian G. Borş, Kun ZhangAAAI · University of Electronic Science and Technology of China · University of York · +2
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  4. 2025
    Lifelong Scalable Generative System via Online Maximum Mean DiscrepancyFei Ye, Adrian G. BorşAAAI · University of Electronic Science and Technology of China · University of York
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  5. 2024PDF ↗
  6. 2024
    Task-Free Dynamic Sparse Vision Transformer for Continual LearningFei Ye, Adrian G. BorşAAAI · Mohamed bin Zayed University of Artificial Intelligence · University of York
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  7. 2023PDF ↗
  8. 2023
    Learning Dynamic Latent Spaces for Lifelong Generative ModellingFei Ye, Adrian G. BorşAAAI · University of York
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  9. 2023
    Lifelong Compression Mixture Model via Knowledge Relationship GraphFei Ye, Adrian G. BorşAAAI · University of York
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  10. 2023PDF ↗
  11. 2021
    Lifelong Generative Modelling Using Dynamic Expansion Graph ModelFei Ye, Adrian G. BorşAAAI · University of York
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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.