Related work

The foundational work on continual learning, 1959 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

11 papers of 11,817Sort Recent · Most cited
  1. 2021
    Backdoor learning curves: explaining backdoor poisoning beyond influence functionsAntonio Emanuele Ciná, Kathrin Grosse, Sebastiano Vascon … Marcello PelilloInternational Journal of Machine Learning and Cybernetics · University of Genoa · École Polytechnique Fédérale de Lausanne · +2
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  2. 2022
    Fast adaptation to rule switching using neuronal surpriseMartin Barry, Wulfram GerstnerbioRxiv · École Polytechnique Fédérale de Lausanne
  3. 2022
    Incremental Learning in Diagonal Linear NetworksRaphaël BerthierJMLR · École Polytechnique Fédérale de Lausanne
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  4. 2022
    Continual Test-Time Domain AdaptationQin Wang, Olga Fink, Luc Van Gool, Dengxin DaiCVPR · ETH Zurich · École Polytechnique Fédérale de Lausanne · +1
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  5. 2020
    ADER: Adaptively Distilled Exemplar Replay Towards Continual Learning for Session-based RecommendationFei Mi, Xiaoyu Lin, Boi FaltingsRecSys · École Polytechnique Fédérale de Lausanne
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  6. 2020
    Memory Augmented Neural Model for Incremental Session-based RecommendationFei Mi, Boi FaltingsIJCAI · École Polytechnique Fédérale de Lausanne
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  7. 2020
    Understanding Regularisation Methods for Continual LearningFrederik BenzingarXiv · École Polytechnique Fédérale de Lausanne
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  8. 2020
    On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong BaselinesMarius Mosbach, Maksym Andriushchenko, Dietrich KlakowICLR · Saarland University · École Polytechnique Fédérale de Lausanne
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  9. 2020
    Generalized Class Incremental LearningFei Mi, Lingjing Kong, Tao Lin … Boi FaltingsCVPR · École Polytechnique Fédérale de Lausanne
  10. 2020
    Continual Learning for Natural Language Generation in Task-oriented Dialog SystemsFei Mi, Liangwei Chen, Mengjie Zhao … Boi FaltingsEMNLP · École Polytechnique Fédérale de Lausanne · LMU Klinikum · +2
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  11. 2019
    Synaptic weight decay with selective consolidation enables fast learning without catastrophic forgettingPascal Leimer, Michael H. Herzog, Walter SennbioRxiv · University of Bern · École Polytechnique Fédérale de Lausanne
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.