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.

5 papers of 8,653Sort Recent · Most cited
  1. 2026
    In Situ Training of Implicit Neural Compressors for Scientific Simulations via Sketch-Based RegularizationCooper Simpson, Stephen Becker, Alireza DoostanJournal of Computational Physics · University of Colorado Boulder · University of Washington Applied Physics Laboratory · +2
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  2. 2025
    Hippocampal indexing alters the stability landscape of synaptic weight space allowing life-long learningOscar C. González, Ryan Golden, Erik Delanois … Maxim BazhenovbioRxiv · University of Colorado Boulder · University of California San Diego · +3
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  3. 2024
    Parameter Efficient Fine-tuning of Self-supervised ViTs without Catastrophic ForgettingReza Akbarian Bafghi, Nidhin Harilal, Claire Monteleoni, Maziar RaissiCVPR · University of Colorado Boulder · University of Colorado System · +1
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  4. 2023
    Utilizing Priming to Identify Optimal Class Ordering to Alleviate Catastrophic ForgettingGabriel Mantione-Holmes, Justin Leo, Jugal KalitaIEEE 17th International Conference on Semantic Computing… · Lewis & Clark College · University of Colorado Boulder · +2
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  5. 2021
    Wandering within a World: Online Contextualized Few-Shot LearningMengye Ren, Michael L. Iuzzolino, Michael C. Mozer, Richard S. ZemelICLR · University of Toronto · University of Colorado Boulder · +1
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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.