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.

16 papers of 11,817Sort Recent · Most cited
  1. 2021
    A study on the plasticity of neural networksTudor Berariu, Wojciech Marian Czarnecki, Soham De … Claudia ClopatharXiv · Imperial College London
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  2. 2021
    Rational LAMOL: A Rationale-based Lifelong Learning FrameworkKasidis Kanwatchara, Thanapapas Horsuwan, Piyawat Lertvittayakumjorn … Peerapon VateekulACL · Chulalongkorn University · Imperial College London
  3. 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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  4. 2021
    Internet of emotional people: Towards continual affective computing cross cultures via audiovisual signalsJing Han, Zixing Zhang, Maja Pantić, Björn W. SchullerFuture Generation Computer Systems · University of Augsburg · Imperial College London
  5. 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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  6. 2020
    Continual Reinforcement Learning with Multi-Timescale ReplayChristos Kaplanis, Claudia Clopath, Murray ShanahanarXiv · Imperial College London
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  7. 2020
    Average Jane, Where Art Thou? – Recent Avenues in Efficient Machine Learning Under Subjectivity UncertaintyGeorgios Rizos, Björn W. SchullerSpringer CCIS · Imperial College London · University of Augsburg · +1
  8. 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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  9. 2019
    Policy Consolidation for Continual Reinforcement LearningChristos Kaplanis, Murray Shanahan, Claudia ClopathICML · Imperial College London
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  10. 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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  11. 2018
    Continual Reinforcement Learning with Complex SynapsesChristos Kaplanis, Murray Shanahan, Claudia ClopathICML · Imperial College London
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  12. 2016
    Overcoming catastrophic forgetting in neural networksJames Kirkpatrick, Razvan Pascanu, Neil C. Rabinowitz … Raia HadsellPNAS · Google DeepMind (United Kingdom) · Imperial College London
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  13. 2014
    A morphable template framework for robot learning by demonstration: Integrating one-shot and incremental learning approachesYan Wu, Yanyu Su, Yiannis DemirisRobotics and Autonomous Systems · Agency for Science, Technology and Research · Institute for Infocomm Research · +2
  14. 2012
    Incremental learning of an optical flow model for sensorimotor anticipation in a mobile robotArturo Ribes, Jesús Cerquides, Yiannis Demiris, Ramón López de MántarasIEEE International Conference on Development and Learning… · Consejo Superior de Investigaciones Científicas · Imperial College London
  15. 2012
    Euler Principal Component AnalysisStephan Liwicki, Georgios Tzimiropoulos, Stefanos Zafeiriou, Maja PantićInternational Journal of Computer Vision · Imperial College London · University of Lincoln · +1
  16. 2011
    Towards incremental learning of task-dependent action sequences using probabilistic parsingKyuhwa Lee, Yiannis DemirisIEEE International Conference on Development and Learning… · Imperial College London
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.