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.

5 papers of 11,817Sort Recent · Most cited
  1. 2021
    Self-Activating Neural Ensembles for Continual Reinforcement LearningSam Powers, Xing, Eliot, Gupta, AbhinavCoLLAs · Carnegie Mellon University · Meta (Israel)
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  2. 2021
    Block Contextual MDPs for Continual LearningShagun Sodhani, Franziska Meier, Joëlle Pineau, Amy ZhangConference on Learning for Dynamics & Control · Meta (Israel)
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  3. 2021
    TyXe: Pyro-based Bayesian neural nets for PytorchHippolyt Ritter, Theofanis KaraletsosConference on Machine Learning and Systems · University College London · Meta (Israel)
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  4. 2021
    Adapting BERT for Continual Learning of a Sequence of Aspect Sentiment Classification TasksZixuan Ke, Hu Xu, Bing LiuNAACL · University of Illinois Chicago · Meta (Israel)
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  5. 2021
    CLASSIC: Continual and Contrastive Learning of Aspect Sentiment Classification TasksZixuan Ke, Bing Liu, Hu Xu, Lei ShuEMNLP · University of Illinois Chicago · Meta (Israel) · +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 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.