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.

4 papers of 11,817Sort Recent · Most cited
  1. 2022
    Class-wise Classifier Design Capable of Continual Learning using Adaptive Resonance Theory-based Topological ClusteringNaoki Masuyama, Yusuke Nojima, Farhan Dawood, Zongying LiuApplied Sciences · Osaka Metropolitan University · Tokyo Metropolitan University · +3
    PDF ↗
  2. 2022
    A Robust Growing Memory Network for Lifelong Learning of Intelligent AgentsWei Hong Chin, Wenbang Dou, Naoyuki Kubota, Chu Kiong LooIJCNN · Tokyo Metropolitan University · University of Malaya
  3. 2021
    Lifelong Robot Edutainment based on Self-EfficacyRino Kaburagi, Yudai Ishimaru, Wei Hong Chin … Simon EgertonIEEE International Conference on Cybernetics (CYBCONF) · Tokyo Metropolitan University · La Trobe University
  4. 2019
    Spatial Map Learning with Self-Organizing Adaptive Recurrent Incremental NetworkWei Hong Chin, Naoyuki Kubota, Chu Kiong Loo … Honghai LiuIJCNN · Tokyo Metropolitan University · University of Malaya · +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.