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.

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C. L. Philip Chen South China University of Technology Clear all

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
2021
Frequency Principle in Broad Learning SystemGuangyong Chen, Min Gan, C. L. Philip Chen … Long ChenTNNLS · Fuzhou University · South China University of Technology · +1
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
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
2020
Weighted Generalized Cross-Validation-Based Regularization for Broad Learning SystemMin Gan, Hongtao Zhu, Guangyong Chen, C. L. Philip ChenIEEE Trans. Cybernetics · Fuzhou University · University of Macau · +1
2020
Neural-Learning-Based Force Sensorless Admittance Control for Robots With Input DeadzoneGuangzhu Peng, C. L. Philip Chen, Wei He, Chenguang YangIEEE Transactions · University of Macau · South China University of Technology · +3
2020
Sparse Bayesian Broad Learning System for Probabilistic Estimation of PredictionLili Xu, C. L. Philip Chen, Ruizhi HanIEEE Access · University of Macau · Beijing Normal University · +2
2019
Multiview High Dynamic Range Image Synthesis Using Fuzzy Broad Learning SystemHongbin Guo, Bin Sheng, Ping Li, C. L. Philip ChenIEEE Trans. Cybernetics · Shanghai Jiao Tong University · Hong Kong Polytechnic University · +3

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.