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.

16 papers of 11,817Sort Recent · Most cited
  1. 2024
    In-context Continual Learning Assisted by an External Continual LearnerS. Momeni, Sahisnu Mazumder, Zixuan Ke, Bing LiuCOLING
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  2. 2024PDF ↗
  3. 2023
    Sub-network Discovery and Soft-masking for Continual Learning of Mixed TasksZixuan Ke, Bing Liu, Wenhan Xiong … Haoran LiEMNLP
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  4. 2023
    Parameter-Level Soft-Masking for Continual LearningTatsuya Konishi, M. Kurokawa, C. Ono … Bin LiuICML
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  5. 2023
    Open-World Continual Learning: Unifying Novelty Detection and Continual LearningGyuhak Kim, Changnan Xiao, Tatsuya Konishi … Bin LiuArtificial Intelligence
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  6. 2023
    Continual Pre-training of Language ModelsZixuan Ke, Yijia Shao, Haowei Lin … Bin LiuICLR
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  7. 2023
    Continual Learning of Language ModelsZixuan Ke, Yijia Shao, Haowei Lin … Bin LiuICLR
  8. 2022PDF ↗
  9. 2022
    A Theoretical Study on Solving Continual LearningGyuhak Kim, Changnan Xiao, Tatsuya Konishi … Bing LiuNeurIPS
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  10. 2022PDF ↗
  11. 2022
  12. 2021PDF ↗
  13. 2021PDF ↗
  14. 2021
    Adapting BERT for Continual Learning of a Sequence of Aspect Sentiment Classification TasksZixuan Ke, Hu Xu, Bing LiuNAACL · University of Illinois Chicago · Meta (Israel)
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  15. 2021
    CLASSIC: Continual and Contrastive Learning of Aspect Sentiment Classification TasksZixuan Ke, Bing Liu, Hu Xu, Lei ShuEMNLP · University of Illinois Chicago · Meta (Israel) · +1
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  16. 2021
    Continual Learning with Knowledge Transfer for Sentiment ClassificationZixuan Ke, Bing Liu, Hao Wang, Lei ShuSpringer LNCS · University of Illinois Chicago · Southwest Jiaotong University
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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.