Related work

The foundational work on continual learning, 1988 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

6 papers of 6,984Sort Recent · Most cited
  1. 2022
    Three types of incremental learningGido M. van de Ven, Tinne Tuytelaars, Andreas S. ToliasNature Machine Intelligence · Baylor College of Medicine · University of Cambridge · +2
  2. 2022
    Continual Learning for Affective Robotics: A Proof of Concept for WellbeingNikhil Churamani, Minja Axelsson, Atahan Çaldır, Hatice GüneşInternational Conference on Affective Computing and Intel… · University of Cambridge · Özyeğin University
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  3. 2022
    Domain-Incremental Continual Learning for Mitigating Bias in Facial Expression and Action Unit RecognitionNikhil Churamani, Özgür Kara, Hatice GüneşIEEE Transactions · University of Cambridge · Boğaziçi University
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  4. 2022
    Mind Your Manners! A Dataset and a Continual Learning Approach for Assessing Social Appropriateness of Robot ActionsJonas Tjomsland, Sinan Kalkan, Hatice GüneşFrontiers · University of Cambridge · Middle East Technical University
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  5. 2022
    Improving Scheduled Sampling with Elastic Weight Consolidation for Neural Machine TranslationMichalis Korakakis, Andreas VlachosEMNLP · University of Cambridge
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  6. 2022
    Provable Lifelong Learning of RepresentationsXinyuan Cao, Weiyang Liu, Santosh VempalaAISTATS · Georgia Institute of Technology · 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. 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. 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.