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
  1. 2021
    Federated Continuous Learning With Broad Network ArchitectureJunqing Le, Xinyu Lei, Nankun Mu … Xiaofeng LiaoIEEE Trans. Cybernetics · Southwest University · Michigan State University · +2
  2. 2021
    Forecasting of Credit Card Default Based on Incremental Random ForestXiang Gao, Junhao Wen, Cheng ZhangJournal of Physics Conference Series · Chongqing University
  3. 2020
    Prototype-Based Discriminative Feature Representation for Class-incremental Cross-modal RetrievalShaoquan Zhu, Yong Feng, Mingliang Zhou … Ran WeiInternational Journal of Pattern Recognition and Artifici… · Ministry of Education of the People's Republic of China · Chongqing University · +3
  4. 2020
    A Novel Broad Learning Model-Based Semi-Supervised Image Classification MethodJianjie Zheng, Yu Yuan, Huimin Zhao, Wu DengIEEE Access · Dalian Jiaotong University · Chongqing University · +2
  5. 2008
    Some practical aspects on incremental training of RBF network for robot behavior learningJun Li, Tom DuckettWorld Congress on Intelligent Control and Automation · Chongqing University · University of Lincoln
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.