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.

7 papers of 11,817Sort Recent · Most cited
  1. 2021
    Continual Learning for Sentiment Classification by Iterative Networks CombinationShupeng Wang, Junhao LiuInternational Conference on Computer Science and Artifici… · University of Science and Technology of China · Shenzhen Institutes of Advanced Technology
  2. 2021
    Task Ordering Matters for Incremental LearningZhaonan Yang, Huiyun LiInternational Symposium on Networks, Computers and Commun… · Shenzhen Institutes of Advanced Technology · University of Chinese Academy of Sciences · +1
  3. 2021
    Flattening Sharpness for Dynamic Gradient Projection Memory Benefits Continual LearningDanruo Deng, Guangyong Chen, Jianye Hao … Pheng‐Ann HengNeurIPS · Chinese University of Hong Kong · Shenzhen Institutes of Advanced Technology · +1
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  4. 2021
    Iterative Network Pruning with Uncertainty Regularization for Lifelong Sentiment ClassificationBinzong Geng, Min Yang, Fajie Yuan … Ruifeng XuSIGIR · University of Science and Technology of China · Chinese Academy of Sciences · +5
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  5. 2021
    Layerwise Optimization by Gradient Decomposition for Continual LearningShixiang Tang, Dapeng Chen, Jinguo Zhu … Wanli OuyangCVPR · The University of Sydney · Group Sense (China) · +2
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  6. 2021
    Continual Learning for Task-oriented Dialogue System with Iterative Network Pruning, Expanding and MaskingBinzong Geng, Fajie Yuan, Qiancheng Xu … Min YangACL · University of Science and Technology of China · Chinese Academy of Sciences · +7
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  7. 2020
    Learning Stable Control for a Wheeled Inverted Pendulum with Fast Adaptive Neural NetworkYuanzhe Peng, Yongsheng Ou, Wei FengIEEE International Conference on Real-time Computing and… · Chinese Academy of Sciences · Shenzhen Institutes of Advanced Technology · +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. 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.