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. 2019
    Continual Learning Using Bayesian Neural NetworksHonglin Li, Payam Barnaghi, Shirin Enshaeifar, Frieder GanzTNNLS · University of Surrey · UK Dementia Research Institute · +1
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  2. 2019
    OpenLORIS-Object: A Robotic Vision Dataset and Benchmark for Lifelong Deep LearningQi She, Fan Feng, Xinyue Hao … Rosa H. M. ChanICRA · City University of Hong Kong · Tsinghua University · +4
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  3. 2019
    Memory-Efficient Episodic Control Reinforcement Learning with Dynamic Online k-meansAndrea Agostinelli, Kai Arulkumaran, Marta Sarrico … Anil A. BharatharXiv · Imperial College London
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  4. 2019
    Policy Consolidation for Continual Reinforcement LearningChristos Kaplanis, Murray Shanahan, Claudia ClopathICML · Imperial College London
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  5. 2019
    Rates of Convergence for Sparse Variational Gaussian Process RegressionDavid R. Burt, Carl Edward Rasmussen, Mark van der WilkICML · University of Cambridge · Imperial College London
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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.