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. 2019
    Event-Based Incremental Broad Learning System for Object ClassificationShan Gao, Guangqian Guo, C. L. Philip ChenIEEE Conference Proceedings · Northwestern Polytechnical University
  2. 2019
    IDNS: A High-Performance Model for Identification of DNS Infrastructures on Large-scale TrafficCaiyun Huang, Yujia Zhu, Yong Sun … Binxing FangIEEE Conference Proceedings · Chinese Academy of Sciences · Institute of Information Engineering · +1
  3. 2018
    Bitcoin Volatility Forecasting with a Glimpse into Buy and Sell OrdersTian Guo, Albert Bifet, Nino Antulov-FantulinIEEE Conference Proceedings · ETH Zurich · Télécom Paris
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  4. 2016
    Incremental learning for bootstrapping object classifier modelsCem Karaoguz, Alexander GepperthIEEE Conference Proceedings · Institut national de recherche en sciences et technologies du numérique · École Nationale Supérieure de Techniques Avancées · +1
  5. 2016
    Incremental learning of neural network classifiers using reinforcement learningSourabh Bose, Manfred HuberIEEE Conference Proceedings · The University of Texas at Arlington
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.