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.

7 papers of 8,653Sort Recent · Most cited
  1. 2023
    Diagnosing Catastrophe: Large parts of accuracy loss in continual learning can be accounted for by readout misalignmentDaniel Anthes, Sushrut Thorat, Peter König, Tim C KietzmannConference on Cognitive Computational Neuroscience
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  2. 2023
    Toward a More Neurally Plausible Neural Network Model of Latent Cause InferenceQihong Lu, Tan Tien Nguyen, Uri Hasson … Kenneth A. NormanConference on Cognitive Computational Neuroscience · Princeton University · Washington University in St. Louis · +1
  3. 2023
    Efficient Continual Learning in Reservoir NetworksPaul Okeahalam, Liang Zhou, Jorge Aurelio Menendez, Peter E. LathamConference on Cognitive Computational Neuroscience · University College London
  4. 2023
    Humans and Neural Networks Show Similar Patterns of Transfer and Interference in a Continual Learning TaskEleanor Holton, Lukas Braun, Jessica A. F. Thompson, Christopher SummerfieldConference on Cognitive Computational Neuroscience · University of Oxford
  5. 2023
    Rapid Learning Without Catastrophic Forgetting in Multiple Morris Water MazesRaymond Wang, Jaedong Hwang, Akhilan Boopathy, Ila FieteConference on Cognitive Computational Neuroscience · Massachusetts Institute of Technology
  6. 2022
    Continual Reinforcement Learning with Multi-Timescale Successor FeaturesRaymond Chua, Blake Richards, Doina Precup, Christos KaplanisConference on Cognitive Computational Neuroscience · McGill University · Google DeepMind (United Kingdom)
  7. 2019
    Automatically inferring task context for continual learningJasmine Collins, Kelvin Xu, Bruno A. Olshausen, Brian CheungConference on Cognitive Computational Neuroscience · University of California, Berkeley
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.