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.

10 papers of 11,817Sort Recent · Most cited
  1. 2026
    Leveraging Information Flow for Knowledge Transfer in Continual LearningJoshua Andle, Ali Payani, Salimeh Yasaei-SekehNeural Processing Letters
  2. 2025
    From task-aware to task-agnostic parameter isolation for incremental learningAlex Vicente-Sola, Paul Kirkland, G. Di Caterina … Marc MasanaNeural Processing Letters
  3. 2025
  4. 2025
    Continual Learning in Medicine: A Systematic Literature ReviewPierangela Bruno, A. Quarta, Francesco CalimeriNeural Processing Letters
  5. 2024
    SS-CRE: A Continual Relation Extraction Method Through SimCSE-BERT and Static Relation PrototypesJin-Guang Chen, Suyue Wang, Li-Li Ma … Kaibing ZhangNeural Processing Letters
  6. 2024
    A Time-Series-Based Sample Amplification Model for Data Stream with Sparse SamplesJun-Cheng Yang, Wei Yu, Fang Yu, Shijun LiNeural Processing Letters
  7. 2023
    TLCE: Transfer-Learning Based Classifier Ensembles for Few-Shot Class-Incremental LearningShuang-Mei Wang, Yang Cao, Tieru WuNeural Processing Letters
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  8. 2023
  9. 2006
    A Neural Model for Context-dependent Sequence LearningLuc Berthouze, Adriaan TijsselingNeural Processing Letters · Food Research Institute · National Institute of Advanced Industrial Science and Technology
  10. 2001
    Incremental Learning with Respect to New Incoming Input AttributesSheng-Uei Guan, Shanchun LiNeural Processing Letters · National University of Singapore
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.