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.

6 papers of 8,653Sort Recent · Most cited
  1. 2021
    SPeCiaL: Self-Supervised Pretraining for Continual LearningLucas Caccia, Joëlle PineauSpringer LNCS · McGill University
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  2. 2021
    Impact Patterns of Combining Model Pruning and Continual Learning on Model PerformanceXueyang Zhang, Hang Li, Xi Chen, Xue LiuIEEE Third International Conference on Cognitive Machine… · McGill University
  3. 2021
    Structure Aware Experience Replay for Incremental Learning in Graph-based Recommender SystemsKian Ahrabian, Yishi Xu, Yingxue Zhang … Mark CoatesACM International Conference on Information & Knowled… · McGill University · Huawei Technologies (Canada)
  4. 2021
    Learning offline: memory replay in biological and artificial reinforcement learningEmma L. Roscow, Raymond Chua, Rui Ponte Costa … Nathan F. LeporaTrends in Neurosciences · Centre de Recerca Matemàtica · McGill University · +2
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  5. 2021
    TIE: A Framework for Embedding-based Incremental Temporal Knowledge Graph CompletionJiapeng Wu, Yishi Xu, Yingxue Zhang … Jackie Chi Kit CheungSIGIR · McGill University · Université de Montréal · +1
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  6. 2021
    Understanding Capacity Saturation in Incremental LearningShenyang Huang, Vincent François-Lavet, Guillaume RabusseauCanadian Conference on Artificial Intelligence · Centre Universitaire de Mila · McGill University · +3
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.