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.

5 papers of 11,817Sort Recent · Most cited

TNNLS 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
2022
KNNENS: A k-Nearest Neighbor Ensemble-Based Method for Incremental Learning Under Data Stream With Emerging New ClassesJianjun Zhang, Ting Wang, Wing W. Y. Ng, Witold PedryczTNNLS · South China University of Technology · Guangzhou First People's Hospital · +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

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.