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.

17 papers of 8,653Sort Recent · Most cited
  1. 2022
    G-MAP: General Memory-Augmented Pre-trained Language Model for Domain TasksZhongwei Wan, Yichun Yin, Wei Zhang … Liu, QunEMNLP
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  2. 2022
    Semi-Supervised Lifelong Language LearningYingxiu Zhao, Yinhe Zheng, Bowen Yu … Nevin L. ZhangEMNLP
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  7. 2022
    Federated Continual Learning for Text Classification via Selective Inter-client TransferYatin Chaudhary, Pranav Rai, Matthias Schubert … Pankaj GuptaEMNLP
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  8. 2022
    Continual Training of Language Models for Few-Shot LearningZixuan Ke, Haowei Lin, Yijia Shao … Bing LiuEMNLP
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  11. 2022
    Overcoming Catastrophic Forgetting in Zero-Shot Cross-Lingual GenerationTu Vu, Aditya Barua, Brian Lester … Noah ConstantEMNLP
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  12. 2022PDF ↗
  13. 2022
    Fine-tuned Language Models are Continual LearnersThomas Scialom, Tuhin Chakrabarty, Smaranda MuresanEMNLP · University of Missouri · Columbia University
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  14. 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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  15. 2022
    CLLE: A Benchmark for Continual Language Learning Evaluation in Multilingual Machine TranslationHan Zhang, Sheng Zhang, Yang Xiang … Ruifeng XuEMNLP · Harbin Institute of Technology · Peng Cheng Laboratory · +3
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  16. 2022
    Entropy-Based Vocabulary Substitution for Incremental Learning in Multilingual Neural Machine TranslationKaiyu Huang, Peng Li, Jin Ma, Yang LiuEMNLP · Tsinghua University · Peng Cheng Laboratory · +5
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  17. 2022
    Improving Scheduled Sampling with Elastic Weight Consolidation for Neural Machine TranslationMichalis Korakakis, Andreas VlachosEMNLP · University of Cambridge
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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.