Related work

The foundational work on continual learning, 1980 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

11 papers of 8,653Sort Recent · Most cited
  1. 2025
    When Data Falls Short: Grokking Below the Critical ThresholdVaibhav Singh, Eugene Belilovsky, Rahaf AljundiarXiv
    PDF ↗
  2. 2024
    Dual-Phase Continual Learning: Supervised Adaptation Meets Unsupervised RetentionVaibhav Singh, Rahaf Aljundi, Eugene BelilovskyTrans. Mach. Learn. Res.
    PDF ↗
  3. 2024
    Simple and Scalable Strategies to Continually Pre-train Large Language ModelsAdam Ibrahim, Benjamin Therien, Kshitij Gupta … Irina RishTrans. Mach. Learn. Res.
    PDF ↗
  4. 2023PDF ↗
  5. 2023
    Prototype-Sample Relation Distillation: Towards Replay-Free Continual LearningNader Asadi, MohammadReza Davar, Sudhir P. Mudur … Eugene BelilovskyICML
    PDF ↗
  6. 2022
    Probing Representation Forgetting in Supervised and Unsupervised Continual LearningMohammadReza Davari, Nader Asadi, Sudhir P. Mudur … Eugene BelilovskyCVPR · Concordia University · Toyota Motor Corporation (Switzerland)
    PDF ↗
  7. 2022
    Tackling Online One-Class Incremental Learning by Removing Negative ContrastsNader Asadi, Sudhir P. Mudur, Eugene BelilovskyarXiv
    PDF ↗
  8. 2021
    New Insights on Reducing Abrupt Representation Change in Online Continual LearningLucas Caccia, Rahaf Aljundi, Nader Asadi … Eugene BelilovskyICLR
    PDF ↗
  9. 2021
    Reducing Representation Drift in Online Continual LearningLucas Caccia, Rahaf Aljundi, T. Tuytelaars … Eugene BelilovskyarXiv
  10. 2019
    Online Learned Continual Compression with Stacked Quantization ModuleLucas Caccia, Eugene Belilovsky, M. Caccia, Joëlle PineauarXiv · McGill University · Meta (Israel)
    PDF ↗
  11. 2019
    Online Continual Learning with Maximally Interfered RetrievalRahaf Aljundi, Lucas Caccia, Eugene Belilovsky … Tinne TuytelaarsarXiv · McGill University · Université de Montréal · +1
    PDF ↗
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.