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. 2024PDF ↗
  2. 2023
    Cross-lingual Continual LearningMeryem M’hamdi, Xiang Ren, Jonathan MayACL · University of Southern California · California Southern University
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  3. 2022
    On Continual Model Refinement in Out-of-Distribution Data StreamsBill Lin, Sida Wang, Xi Victoria Lin … Wen-tau YihACL
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  4. 2022
    Lifelong Pretraining: Continually Adapting Language Models to Emerging CorporaXisen Jin, Dejiao Zhang, Henghui Zhu … Xiang RenNAACL · University of Southern California · California Southern University
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  5. 2021
    Learn Continually, Generalize Rapidly: Lifelong Knowledge Accumulation for Few-shot LearningXisen Jin, Bill Yuchen Lin, Mohammad Rostami, Xiang RenEMNLP · University of Southern California · Biomedical Research Institute of Southern California · +1
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  6. 2021PDF ↗
  7. 2020
    Visually Grounded Continual Learning of Compositional SemanticsXisen Jin, Junyi Du, Arka Sadhu … Xiang RenarXiv · University of Southern California · California Southern University
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  8. 2020
    Visually Grounded Continual Learning of Compositional PhrasesXisen Jin, Junyi Du, Arka Sadhu … Xiang RenEMNLP · University of Southern California · California Southern University · +1
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.