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.

11 papers of 8,653Sort Recent · Most cited
  1. 2026
    Discrete synaptic states and context-modulated readouts support continual learningYuan Gao, Ştefan Mihalaş, Denis TurcubioRxiv · University of Washington · Allen Institute
  2. 2026
    Space-based Parameter Evolving with Lightweight Optimization for Graph Adaptation to Evolving ShiftsJunyu Luo, Zixuan Ouyang, Xiao Luo … Ming ZhangACM Web Conference 2026 · Peking University · University of Wisconsin–Madison · +2
  3. 2025
    Gradient-Guided Epsilon Constraint Method for Online Continual LearningSong Lai, Changyi Ma, Fei Zhu … Qingfu ZhangNeurIPS · City University of Hong Kong · Chinese University of Hong Kong · +4
  4. 2024
    Distribution-Aware Continual Test-Time Adaptation for Semantic SegmentationJiayi Ni, Senqiao Yang, Ran Xu … Shanghang ZhangICRA · King University · Peking University · +2
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  5. 2024
    ESA: Expert-and-Samples-Aware Incremental Learning Under Longtail DistributionJie Mei, Jenq–Neng HwangICASSP · University of Washington
  6. 2023
    TKIL: Tangent Kernel Optimization for Class Balanced Incremental LearningJinlin Xiang, Eli ShlizermanICCV · University of Washington · Seattle University
  7. 2023
    Signatures of task learning in neural representations.Harsha Gurnani, N. Alex Cayco-GajicCurrent Opinion in Neurobiology · Twitter (United States) · University of Washington · +3
  8. 2022
    Task Adaptive Parameter Sharing for Multi-Task LearningMatthew Wallingford, Hao Li, Alessandro Achille … Stefano SoattoCVPR · University of Washington
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  9. 2023
    A biologically inspired architecture with switching units can learn to generalize across backgroundsDoris Voina, Eric Shea‐Brown, Ştefan MihalaşNeural Networks · University of Washington · University of Washington Applied Physics Laboratory · +2
  10. 2019
    Online Meta-LearningChelsea Finn, Aravind Rajeswaran, Sham M. Kakade, Sergey LevineICML · Stanford University · University of Washington · +3
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  11. 2016
    Net2Net: Accelerating Learning via Knowledge TransferTianqi Chen, Ian Goodfellow, Jonathon ShlensICLR · University of Washington · Google (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. By default it shows the papers we have a reason to trust: published at a venue like NeurIPS, ICML, ICLR, CVPR or TPAMI, or written by someone who has published there, or cited a few hundred times. The rest are one click away under “All papers”. 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.