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.

5 papers of 8,653Sort Recent · Most cited
  1. 2023
    Sub-network Discovery and Soft-masking for Continual Learning of Mixed TasksZixuan Ke, Bing Liu, Wenhan Xiong … Haoran LiEMNLP
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  2. 2023
    Parameter-Level Soft-Masking for Continual LearningTatsuya Konishi, Mori Kurokawa, Chihiro Ono … Bing LiuICML
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  3. 2023
    Open-World Continual Learning: Unifying Novelty Detection and Continual LearningGyuhak Kim, Changnan Xiao, Tatsuya Konishi … Bing LiuArtificial Intelligence
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  4. 2023
    Continual Pre-training of Language ModelsZixuan Ke, Yijia Shao, Haowei Lin … Bing LiuICLR
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  5. 2023
    Continual Learning of Language ModelsZixuan Ke, Yijia Shao, Haowei Lin … Bin LiuICLR
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. By default it shows the papers we have a reason to trust: published at a venue like NeurIPS, ICML, ICLR, CVPR or TPAMI, or written by someone who has published there, or cited a few hundred times. The rest are one click away under “All papers”. 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.