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. 2021
    Continual Learning in Task-Oriented Dialogue SystemsAndrea Madotto, Zhaojiang Lin, Zhenpeng Zhou … Zhiguang WangEMNLP · Hong Kong University of Science and Technology · Meta (Israel) · +1
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  2. 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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  3. 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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  4. 2021
    Lifelong Event Detection with Knowledge TransferPengfei Yu, Heng Ji, Prem NatarajanEMNLP · University of Illinois Urbana-Champaign · Amazon (United States)
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  5. 2021
    Total Recall: a Customized Continual Learning Method for Neural Semantic ParsersZhuang Li, Lizhen Qu, Gholamreza HaffariEMNLP · Monash University
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  6. 2021
    Domain-Lifelong Learning for Dialogue State Tracking via Knowledge Preservation NetworksQingbin Liu, Pengfei Cao, Cao Liu … Jun ZhaoEMNLP · Chinese Academy of Sciences · Shandong Institute of Automation · +5
  7. 2021
    Improving the Quality Trade-Off for Neural Machine Translation Multi-Domain AdaptationEva Hasler, Tobias Domhan, Jonay Trénous … Felix HieberEMNLP · Amazon (Germany)
  8. 2021
    Lifelong Explainer for Lifelong LearnersXuelin Situ, Sameen Maruf, Ingrid Zukerman … Gholamreza HaffariEMNLP · Monash University · Commonwealth Scientific and Industrial Research Organisation · +2
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.