Related work

The foundational work on continual learning, 1959 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

5 papers of 11,817Sort Recent · Most cited
  1. 2022
    Memory Bounds for Continual LearningXi Chen, Christos H. Papadimitriou, Binghui PengIEEE 63rd Annual Symposium on Foundations of Computer Sci… · Columbia University
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  2. 2022
    Learning to Learn and Remember Super Long Multi-Domain Task SequenceZhenyi Wang, Li Shen, Tiehang Duan … Mingchen GaoCVPR · University at Buffalo, State University of New York · Jingdong (China) · +1
  3. 2022
    Biological underpinnings for lifelong learning machinesDhireesha Kudithipudi, Mario Aguilar-Simon, Jonathan Babb … Hava T. SiegelmannNature Machine Intelligence · The University of Texas at San Antonio · Intelligent Systems Research (United States) · +24
  4. 2022
    Fine-tuned Language Models are Continual LearnersThomas Scialom, Tuhin Chakrabarty, Smaranda MuresanEMNLP · University of Missouri · Columbia University
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  5. 2022
    Learning Representations for New Sound Classes With Continual Self-Supervised LearningZhepei Wang, Cem Subakan, Xilin Jiang … Paris SmaragdisIEEE Signal Processing Letters · University of Illinois Urbana-Champaign · Concordia University · +2
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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 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.