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.

8 papers of 8,653Sort Recent · Most cited
  1. 2021
    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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  2. 2021
    Improving Scheduled Sampling with Elastic Weight Consolidation for Neural Machine TranslationMichalis Korakakis, Andreas VlachosEMNLP · University of Cambridge
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  3. 2021
    Provable Lifelong Learning of RepresentationsXinyuan Cao, Weiyang Liu, Santosh VempalaAISTATS · Georgia Institute of Technology · University of Cambridge
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  4. 2021
    Exploring System Performance of Continual Learning for Mobile and Embedded Sensing ApplicationsYoung D. Kwon, Jagmohan Chauhan, Abhishek Kumar … Cecilia MascoloTyöväentutkimus Vuosikirja · University of Cambridge · University of Southampton
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  5. 2021
    Behavioral Experiments for Understanding Catastrophic ForgettingSamuel J. Bell, Neil D. LawrencearXiv · University of Cambridge
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  6. 2021
    FastICARL: Fast Incremental Classifier and Representation Learning with Efficient Budget Allocation in Audio Sensing ApplicationsYoung D. Kwon, Jagmohan Chauhan, Cecilia MascoloInterspeech · University of Cambridge · University of Southampton
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  7. 2021
    Towards Fair Affective Robotics: Continual Learning for Mitigating Bias in Facial Expression and Action Unit RecognitionÖzgür Kara, Nikhil Churamani, Hatice GüneşarXiv · Boğaziçi University · University of Cambridge
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  8. 2021
    Elastic weight consolidation for better bias inoculationJames Thorne, Andreas VlachosEACL · 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 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.