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.

8 papers of 8,653Sort Recent · Most cited
  1. 2025
    PPSEBM: An Energy-Based Model with Progressive Parameter Selection for Continual LearningXiaodi Li, Dingcheng Li, Rujun Gao … Latifur KhanIEEE International Conference on Big Data (BigData) · Mayo Clinic in Florida · Google (United States) · +2
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  2. 2025
    LSEBMCL: A Latent Space Energy-Based Model for Continual LearningXiaodi Li, Dingcheng Li, Rujun Gao … Latifur KhanInternational Conference on Artificial Intelligence in In… · The University of Texas at Dallas · Google (United States) · +1
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  3. 2023
    Power Norm Based Lifelong Learning for Paraphrase GenerationsDingcheng Li, Peng Yang, Yue Zhang, Ping LiSIGIR · Bellevue Hospital Center
  4. 2022
    Latent Coreset Sampling based Data-Free Continual LearningZhuoyi Wang, Dingcheng Li, Ping LiACM International Conference on Information & Knowled… · Bellevue Hospital Center
  5. 2022
    Overcoming Catastrophic Forgetting During Domain Adaptation of Seq2seq Language GenerationDingcheng Li, Zheng Chen, Eunah Cho … Yang LiuNAACL · Amazon (United States)
  6. 2022
    LPC: A Logits and Parameter Calibration Framework for Continual LearningXiaodi Li, Zhuoyi Wang, Dingcheng Li … Bhavani ThuraisinghamEMNLP · The University of Texas at Dallas · Baidu (China)
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  7. 2022
    Continual Learning for Natural Language Generations with Transformer CalibrationPeng Yang, Dingcheng Li, Ping LiCoNLL · Bellevue Hospital Center · Cognitive Research (United States)
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  8. 2021
    CIFDM: Continual and Interactive Feature Distillation for Multi-Label Stream LearningYigong Wang, Zhuoyi Wang, Yu Lin … Dingcheng LiSIGIR · The University of Texas at Dallas · Amazon (United States)
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.