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.

6 papers of 8,653Sort Recent · Most cited
  1. 2026
    Hierarchical learning creates invariant schema within plastic neural networks.James T. Elder, Jie Zheng, Lydia B. Shimelis … Milo M. LinJournal of Computational Neuroscience · Center for Systems Biology · The University of Texas Southwestern Medical Center · +6
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  2. 2025
    An invariant schema emerges within a neural network during hierarchical learning of visual boundariesJames T. Elder, Jie Zheng, Lydia B. Shimelis … Milo M. LinbioRxiv · Center for Systems Biology · The University of Texas Southwestern Medical Center · +5
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  3. 2024
    Engineering flexible machine learning systems by traversing functionally invariant pathsGuruprasad Raghavan, Bahey Tharwat, Surya N. Hari … Matt ThomsonNature Machine Intelligence · California Institute of Technology · Superior Court of California · +1
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  4. 2023
    Online Hyperparameter Optimization for Class-Incremental LearningYaoyao Liu, Yingying Li, Bernt Schiele, Qianru SunAAAI · Johns Hopkins University · Max Planck Institute for Informatics · +2
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  5. 2021
    Solving hybrid machine learning tasks by traversing weight space geodesicsGuruprasad Raghavan, Matt ThomsonarXiv · California Institute of Technology
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  6. 1991
    Incremental learning with rule-based neural networksCharles M. Higgins, R.M. GoodmanIJCNN · California Institute of Technology
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.