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. 2023
    Efficient Self-Supervised Continual Learning with Progressive Task-Correlated Layer FreezingLi Yang, Sen Lin, Fan Zhang … Deliang FanInternational Symposium on Quality Electronic Design (ISQED) · University of North Carolina at Charlotte · University of Houston · +3
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  2. 2024
    Hyb-Learn: A Framework for On-Device Self-Supervised Continual Learning with Hybrid RRAM/SRAM MemoryFan Zhang, Li Yang, Deliang FanACM/IEEE Design Automation Conference · Johns Hopkins University · University of North Carolina at Charlotte
  3. 2022
    Efficient Multi-task Adaption for Crossbar-based In-Memory ComputingFan Zhang, Li Yang, Deliang FanAsilomar Conference on Signals, Systems, and Computers · Arizona State University
  4. 2022
    XST: A Crossbar Column-wise Sparse Training for Efficient Continual LearningFan Zhang, Yang Li, Jian Meng … Deliang FanDesign, Automation & Test in Europe Conference &… · Arizona State University
  5. 2022
    XBM: A Crossbar Column-wise Binary Mask Learning Method for Efficient Multiple Task AdaptionFan Zhang, Yang Li, Jian Meng … Deliang FanAsia and South Pacific Design Automation Conference (ASP-… · Arizona State University
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.