Related work

The foundational work on continual learning, 1988 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

3 papers of 7,070Sort Recent · Most cited
  1. 2021
    Continual Learning Using Bayesian Neural NetworksHonglin Li, Payam Barnaghi, Shirin Enshaeifar, Frieder GanzTNNLS · University of Surrey · UK Dementia Research Institute · +1
    PDF ↗
  2. 2020
    Continual Learning Using Task Conditional Neural NetworksHaoyu Li, Payam Barnaghi, Shirin Enshaeifar, Frieder GanzarXiv
    PDF ↗
  3. 2019
    Continual Learning in Deep Neural Network by Using a Kalman OptimiserHonglin Li, Shirin Enshaeifar, Frieder Ganz, Payam BarnaghiarXiv
    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 lists only papers we have a reason to trust: published at a venue like NeurIPS, ICML, ICLR, CVPR or TPAMI, or written by someone who has published there, or cited a few hundred times. Preprints that later get accepted, and authors who later publish at those venues, are picked up by the weekly run. 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.