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 6,984Sort Recent · Most cited
  1. 2026
    ODGNet: Learning Dynamic Variable Association for Online Time Series ForecastingYushuo Liu, Yulong Wang, Kai WangTKDE · Nankai University
  2. 2026
    Multi-Scale Adaptive Convolutional Graph for Multi-Stream Concept DriftMing Zhou, Jie Lu, Guangquan ZhangTKDE · University of Technology Sydney
  3. 2026
    HADA: Heteroscedastic-Aware Analytical Dynamic Adaptation for Time Series Class-Incremental LearningHan Liu, Fengbin Zhang, Sizhe Huang … Ruidong WangTKDE · Harbin University of Science and Technology · Zhejiang Normal University
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 led 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.