Related work

The foundational work on continual learning, 1991 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

6 papers of 4,574Sort Recent · Most cited
  1. 2020
    Simple Lifelong Learning MachinesJoshua T. Vogelstein, Jayanta Dey, Hayden S. Helm … Carey E. PriebeTPAMI · Johns Hopkins University · Baylor College of Medicine · +1
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  2. 2023
    Cost-effective On-device Continual Learning over Memory Hierarchy with MiroXinyue Ma, Suyeon Jeong, Minjia Zhang … Myeongjae JeonAnnual International Conference on Mobile Computing and N… · Ulsan National Institute of Science and Technology · Microsoft (United States) · +1
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  3. 2021
    K-Adapter: Infusing Knowledge into Pre-Trained Models with AdaptersRuize Wang, Duyu Tang, Nan Duan … Ming ZhouACL · Fudan University · Microsoft (United States) · +1
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  4. 2019
    AutoML @ NeurIPS 2018 challenge: Design and ResultsHugo Jair Escalante, Wei-Wei Tu, Isabelle Guyon … Qiang YangMachine Learning · Gleason (United States) · National Institute of Astrophysics, Optics and Electronics · +8
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  5. 2019
    An Empirical Study of Example Forgetting during Deep Neural Network LearningMariya Toneva, Alessandro Sordoni, Rémi Tachet des Combes … Geoffrey J. GordonICLR · Carnegie Mellon University · Microsoft (United States) · +1
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  6. 2017
    Differentiable Programs with Neural LibrariesAlexander L. Gaunt, Marc Brockschmidt, Nate Kushman, Daniel TarlowICML · Microsoft (United States)
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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. 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, and only papers with a PDF we can point you at, so every title opens the paper itself. 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.