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. 2022
    Continual Learning with Network Intrusion DatasetHyejin Kim, Dong Seong Kim, Jin-Hee Cho … Hyuk LimIEEE International Conference on Big Data (Big Data) · Gwangju Institute of Science and Technology · The University of Queensland · +3
  2. 2022
    Graph Deep Factors for Probabilistic Time-series ForecastingHongjie Chen, Ryan A. Rossi, Kanak Mahadik … Hoda EldardiryACM Transactions · Virginia Tech · Adobe Systems (United States)
  3. 2019
    REFIT: A Unified Watermark Removal Framework For Deep Learning Systems With Limited DataXinyun Chen, Wenxiao Wang, Chris Bender … Dawn SongACM Asia Conference on Computer and Communications Security · University of California, Berkeley · Tsinghua University · +2
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  4. 2019
    Deep Learning for RF Signal Classification in Unknown and Dynamic Spectrum EnvironmentsYi Shi, Kemal Davaslıoğlu, Yalin E. Sagduyu … Gilbert GreenIEEE International Symposium on Dynamic Spectrum Access N… · Intelligent Automation (United States) · Virginia Tech · +1
    PDF ↗
  5. 2018
    Incremental Learning Models of Bike Counts at Bike Sharing SystemsMohammed Almannaa, Mohammed Elhenawy, Feng Guo, Hesham RakhaInternational Conference on Intelligent Transportation Sy… · Virginia Tech Transportation Institute · Virginia Tech · +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.