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.

8 papers of 8,653Sort Recent · Most cited
  1. 2025
    Dedicated Class Subnetworks for SNN Class Incremental LearningKaty Warr, Jonathon Hare, David ThomasNeuro Inspired Computational Elements (NICE) · University of Southampton
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  2. 2025
    Improving Recall in Sparse Associative Memories That Use NeurogenesisKaty Warr, Jonathon Hare, David ThomasNeural Computation · University of Southampton
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  3. 2025
    DT-QFL: Dual-Timeline Quantum Federated Learning With Time-Symmetric Updates, Temporal Memory Kernels, and Reversed Gradient DynamicsKoffka Khan, Khouler KhanIEEE Transactions · University of the West Indies · University of Southampton
  4. 2024
    Continual deep reinforcement learning with task-agnostic policy distillationMuhammad Burhan Hafez, Kerim ErekmenScientific Reports · University of Southampton · Universität Hamburg
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  5. 2024
    Evolutionary Data Subset Selection for Class-Incremental Learning on Memory-Constrained SystemsEpifanios Baikas, Danesh Tarapore, David B. ThomasGenetic and Evolutionary Computation Conference Companion · University of Southampton
  6. 2021
    Lifetime policy reuse and the importance of task capacityDavid M. Bossens, A.J. SobeyAI Communications · University of Southampton · Turing Institute · +1
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  7. 2021
    Exploring System Performance of Continual Learning for Mobile and Embedded Sensing ApplicationsYoung D. Kwon, Jagmohan Chauhan, Abhishek Kumar … Cecilia MascoloTyöväentutkimus Vuosikirja · University of Cambridge · University of Southampton
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  8. 2021
    FastICARL: Fast Incremental Classifier and Representation Learning with Efficient Budget Allocation in Audio Sensing ApplicationsYoung D. Kwon, Jagmohan Chauhan, Cecilia MascoloInterspeech · University of Cambridge · University of Southampton
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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.