Related work

The foundational work on continual learning, 1988 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

14 papers of 6,984Sort Recent · Most cited
  1. 2023
    Replay-enhanced Continual Reinforcement LearningTiantian Zhang, Kevin Shen, Zichuan Lin … Deheng YeTMLR
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  2. 2023PDF ↗
  3. 2023
    Universal Graph Continual LearningThanh D. Hoang, Do Viet Tung, Duy‐Hung Nguyen … Hung LêTMLR
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  4. 2023
    Lightweight Learner for Shared Knowledge Lifelong LearningYunhao Ge, Yuecheng Li, Di Wu … Laurent IttiTMLR
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  5. 2023
    Accelerating Batch Active Learning Using Continual Learning TechniquesArnav Das, Gantavya Bhatt, Megh Manoj Bhalerao … Jeff BilmesTMLR
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  6. 2023PDF ↗
  7. 2023
    SIESTA: Efficient Online Continual Learning with SleepMd Yousuf Harun, Jhair Gallardo, Tyler L. Hayes … Christopher KananTMLR
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  8. 2023
    Improving Continual Learning by Accurate Gradient Reconstructions of the PastErik Daxberger, S. Swaroop, Kazuki Osawa … Mohammad Emtiyaz KhanTMLR
  9. 2023
    Meta Continual Learning on Graphs with Experience ReplayAltay Unal, A. Akgül, M. Kandemir, Gozde UnalTMLR
  10. 2023
    Lifelong Reinforcement Learning with Modulating MasksEseoghene Ben-Iwhiwhu, Saptarshi Nath, Praveen K. Pilly … Andrea SoltoggioTMLR
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  11. 2023
    Continual Learning by Modeling Intra-Class VariationLonghui Yu, Tianyang Hu, Lanqing Hong … Weiyang LiuTMLR
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  12. 2023
    Learn the Time to Learn: Replay Scheduling in Continual LearningMarcus Klasson, Hedvig Kjellström, Cheng ZhangTMLR
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  13. 2023PDF ↗
  14. 2023
    Memory-efficient Reinforcement Learning with Value-based Knowledge ConsolidationQingfeng Lan, Yangchen Pan, Jun Luo, A. Rupam MahmoodTMLR
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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. 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.