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. 2019
    Spiking Neural Predictive Coding for Continual Learning from Data StreamsAlexander G. OrorbiaNeurocomputing · Rochester Institute of Technology
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  2. 2019
    Lifelong Machine Learning with Deep Streaming Linear Discriminant AnalysisTyler L. Hayes, Christopher KananCVPR · Rochester Institute of Technology
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  3. 2019
    REMIND Your Neural Network to Prevent Catastrophic ForgettingTyler L. Hayes, Kushal Kafle, Robik Shrestha … Christopher KananSpringer LNCS · Rochester Institute of Technology · Adobe Systems (United States) · +2
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  4. 2019
    Rethinking Continual Learning for Autonomous Agents and RobotsGerman I. Parisi, Christopher KananarXiv · Universität Hamburg · Rochester Institute of Technology
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  5. 2019
    Task-Based Neuromodulation Architecture for Lifelong LearningAnurag Daram, Dhireesha Kudithipudi, Ángel Yanguas-GilInternational Symposium on Quality Electronic Design (ISQED) · Rochester Institute of Technology · Argonne National Laboratory
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 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.