Related work

The foundational work on continual learning, 1991 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

11 papers of 4,574Sort Recent · Most cited
  1. 2023
    On the Usage of Continual Learning for Out-of-Distribution Generalization in Pre-trained Language Models of CodeMartin Weyssow, Xin Zhou, Kisub Kim … Houari SahraouiACM Joint European Software Engineering Conference and Sy… · Université de Montréal · Singapore Management University
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  2. 2022
    Towards Continual Reinforcement Learning: A Review and PerspectivesKhimya Khetarpal, Matthew Riemer, Irina Rish, Doina PrecupJournal of Artificial Intelligence Research · Google DeepMind (United Kingdom) · McGill University · +2
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  3. 2022
    Tutorial - Continual Learning beyond classificationAlexander Gepperth, Timothée LesortESANN 2022 proceedings · Fulda University of Applied Sciences · Université de Montréal
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  4. 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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  5. 2021
    IIRC: Incremental Implicitly-Refined ClassificationMohamed Abdelsalam, Mojtaba Faramarzi, Shagun Sodhani, Sarath ChandarCVPR · Mila - Quebec Artificial Intelligence Institute · Université de Montréal · +3
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  6. 2021
    Continuous Coordination As a Realistic Scenario for Lifelong LearningHadi Nekoei, Akilesh Badrinaaraayanan, Aaron Courville, Sarath ChandarICML · Centre Universitaire de Mila · Université de Montréal · +1
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  7. 2020
    GraphSAIL: Graph Structure Aware Incremental Learning for Recommender SystemsYishi Xu, Yingxue Zhang, Wei Guo … Mark CoatesCIKM · Université de Montréal · Huawei Technologies (Canada) · +2
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  8. 2020
    La-MAML: Look-ahead Meta Learning for Continual LearningGunshi Gupta, Karmesh Yadav, Liam PaullNeurIPS · Carnegie Mellon University · Université de Montréal
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  9. 2019
    Online Continual Learning with Maximally Interfered RetrievalRahaf Aljundi, Lucas Caccia, Eugene Belilovsky … Tinne TuytelaarsarXiv · McGill University · Université de Montréal · +1
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  10. 2019
    An Empirical Study of Example Forgetting during Deep Neural Network LearningMariya Toneva, Alessandro Sordoni, Rémi Tachet des Combes … Geoffrey J. GordonICLR · Carnegie Mellon University · Microsoft (United States) · +1
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  11. 2014
    An Empirical Investigation of Catastrophic Forgeting in Gradient-Based Neural NetworksIan Goodfellow, Mehdi Mirza, Xiao Da … Yoshua BengioICLR · Département d'Informatique · Université de Montréal · +1
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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, and only papers with a PDF we can point you at, so every title opens the paper itself. 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.