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.

9 papers of 11,817Sort Recent · Most cited
  1. 2021
    SynthASR: Unlocking Synthetic Data for Speech RecognitionAmin Fazel, Wei Yang, Yulan Liu … Jasha DroppoInterspeech · Samsung (South Korea) · Amazon (United States) · +1
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  2. 2021
    Continual Learning for Named Entity RecognitionNatawut Monaikul, Giuseppe Castellucci, Simone Filice, Oleg RokhlenkoAAAI · University of Illinois Chicago · Amazon (Germany)
  3. 2021
    Improving the Quality Trade-Off for Neural Machine Translation Multi-Domain AdaptationEva Hasler, Tobias Domhan, Jonay Trénous … Felix HieberEMNLP · Amazon (Germany)
  4. 2020
    Optimal Continual Learning has Perfect Memory and is NP-hardJeremias Knoblauch, Hisham Husain, Tom DietheICML · University of Warwick · The Alan Turing Institute · +2
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  5. 2020
    Continual Universal Object DetectionXialei Liu, Hao Yang, Avinash Ravichandran … Stefano SoattoarXiv · Universitat Autònoma de Barcelona · Amazon (Germany)
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  6. 2020
    Incremental Meta-Learning via Indirect Discriminant AlignmentQing Liu, Orchid Majumder, Alessandro Achille … Stefano SoattoECCV · Johns Hopkins University · Amazon (Germany)
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  7. 2019
    Facilitating Bayesian Continual Learning by Natural Gradients and Stein GradientsYu Chen, Tom Diethe, Neil D. LawrencearXiv · University of Bristol · Amazon (Germany)
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  8. 2019
    Continual Learning in PracticeTom Diethe, Tom Borchert, Eno Thereska … Neil D. LawrenceNeurIPS · Amazon (Germany)
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  9. 2019
    Continuous Learning for Large-scale Personalized Domain ClassificationHan Li, Jihwan Lee, Sidharth Mudgal … Young‐Bum KimNAACL · University of Wisconsin–Madison · Amazon (Germany)
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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.