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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.

8 papers of 11,817Sort Recent · Most cited
TNNLS C. L. Philip Chen Clear all
  1. 2025
    SGB-Net: Scalable Graph Broad NetworkYue-Bin Xu, C. L. Philip Chen, Meng-Qi Wu, Tong ZhangTNNLS
  2. 2024
    Robust Incremental Broad Learning System for Data Streams of Uncertain ScaleLin-Jun Zhong, C. L. Philip Chen, Ji-Feng Guo, Tong ZhangTNNLS
  3. 2023
    When Broad Learning System Meets Label Noise Learning: A Reweighting Learning FrameworkLicheng Liu, Junhao Chen, Bin Yang … C. L. Philip ChenTNNLS
  4. 2022
    BASS: Broad Network Based on Localized Stochastic SensitivityTing Wang, Mingyang Zhang, Jianjun Zhang … C. L. Philip ChenTNNLS · Guangzhou First People's Hospital · South China University of Technology · +2
  5. 2021
    Frequency Principle in Broad Learning SystemGuangyong Chen, Min Gan, C. L. Philip Chen … Long ChenTNNLS · Fuzhou University · South China University of Technology · +1
  6. 2021
    Face Sketch Synthesis Using Regularized Broad Learning SystemPing Li, Bin Sheng, C. L. Philip ChenTNNLS · Hong Kong Polytechnic University · Shanghai Jiao Tong University · +3
  7. 2020
    A Hybrid Recursive Implementation of Broad Learning With Incremental FeaturesDi Liu, Simone Baldi, Wenwu Yu, C. L. Philip ChenTNNLS · Southeast University · Delft University of Technology · +1
  8. 2019
    Universal Approximation Capability of Broad Learning System and Its Structural VariationsC. L. Philip Chen, Zhulin Liu, Shuang FengTNNLS · University of Macau
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.