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.

14 papers of 11,817Sort Recent · Most cited
  1. 2022
  2. 2022
    Discrete Key-Value BottleneckFrederik Träuble, Anirudh Goyal, Nasim Rahaman … Bernhard SchölkopfICML
    PDF ↗
  3. 2022PDF ↗
  4. 2022PDF ↗
  5. 2022PDF ↗
  6. 2022
    Continual Learning with Guarantees via Weight Interval ConstraintsMaciej Wołczyk, Karol J. Piczak, Bartosz Wójcik … Przemysław SpurekICML
    PDF ↗
  7. 2022
    StreamingQA: A Benchmark for Adaptation to New Knowledge over Time in Question Answering ModelsAdam Liška, Tomáš Kočiský, Elena Gribovskaya … Angeliki LazaridouICML
    PDF ↗
  8. 2022
    Efficient Test-Time Model Adaptation without ForgettingShuaicheng Niu, Jiaxiang Wu, Yifan Zhang … Mingkui TanICML
    PDF ↗
  9. 2022PDF ↗
  10. 2022
    Dataset Condensation with Contrastive SignalsSaehyung Lee, Sanghyuk Chun, Sangwon Jung … Sungroh YoonICML
    PDF ↗
  11. 2022PDF ↗
  12. 2022
    Forget-free Continual Learning with Winning SubnetworksHaeyong Kang, R. Mina, Sultan Rizky Hikmawan Madjid … C. YooICML
  13. 2022
  14. 2022
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.