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. 2019
    Incremental Learning Using a Grow-and-Prune Paradigm With Efficient Neural NetworksXiaoliang Dai, Hongxu Yin, Niraj K. JhaIEEE Transactions · Princeton University
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  2. 2019
    Motion Planning Networks: Bridging the Gap Between Learning-Based and Classical Motion PlannersAhmed H. Qureshi, Yinglong Miao, Anthony Simeonov, Michael C. YipIEEE Transactions · University of California San Diego · Massachusetts Institute of Technology
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  3. 2019
    Sharp Bounds for Genetic Drift in Estimation of Distribution AlgorithmsBenjamin Doerr, Weijie ZhengIEEE Transactions · Centre National de la Recherche Scientifique · École Polytechnique · +4
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  4. 2019
    Hierarchical Lifelong Learning by Sharing Representations and Integrating HypothesisTong Zhang, Guoxi Su, Chunmei Qing … Xiaofen XingIEEE Transactions · South China University of Technology
  5. 2019
    Incremental Spatiotemporal Learning for Online Modeling of Distributed Parameter SystemsZhi Wang, Han‐Xiong LiIEEE Transactions · City University of Hong Kong
  6. 2019
    Guided Policy Search for Sequential Multitask LearningFangzhou Xiong, Biao Sun, Xu Yang … Zhiyong LiuIEEE Transactions · University of Chinese Academy of Sciences · University of Science and Technology Beijing · +4
  7. 2019
    New Incremental Learning Algorithm With Support Vector MachinesJie Xu, Chen Xu, Bin Zou … Xinge YouIEEE Transactions · Hubei University · University of Ottawa · +2
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.