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.

3 papers of 8,653Sort Recent · Most cited
  1. 2024
    Efficient Continual Learning for Small Language Models with a Discrete Key-Value BottleneckAndor Diera, Lukas Galke, Fabian Karl, Ansgar ScherpInternational Conference on Natural Language and Speech P…
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  2. 2023
    Lifelong learning on evolving graphs under the constraints of imbalanced classes and new classesLukas Galke, Iacopo Vagliano, Benedikt Franke … Ansgar ScherpNeural Networks · Max Planck Institute for Psycholinguistics · Amsterdam University Medical Centers · +3
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  3. 2021
    Lifelong Learning of Graph Neural Networks for Open-World Node ClassificationLukas Galke, Benedikt Franke, Tobias Zielke, Ansgar ScherpIEEE International Joint Conference on Neural Network · Christian-Albrechts-Universität zu Kiel
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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. By default it shows the 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. The rest are one click away under “All papers”. 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.