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.

4 papers of 11,817Sort Recent · Most cited
  1. 2022
    Learning to Predict Gradients for Semi-Supervised Continual LearningYan Luo, Yongkang Wong, Mohan Kankanhalli, Qi ZhaoTNNLS · University of Minnesota · Harvard University · +2
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  2. 2022
    Learning to Learn: How to Continuously Teach Humans and MachinesParantak Singh, You Li, Ankur Sikarwar … Mengmi ZhangICCV · Agency for Science, Technology and Research · Nanyang Technological University · +5
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  3. 2022
    Representational drift: Emerging theories for continual learning and experimental future directions.Laura Driscoll, Lea Duncker, Christopher D. HarveyCurrent Opinion in Neurobiology · Stanford University · Howard Hughes Medical Institute · +1
  4. 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
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.