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.

11 papers of 8,653Sort Recent · Most cited
  1. 2024
    Non-Stationary Learning of Neural Networks with Automatic Soft Parameter ResetAlexandre Galashov, Michalis K. Titsias, András György … Maneesh SahaniNeurIPS
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  2. 2024
    Normalization and effective learning rates in reinforcement learningClare Lyle, Zeyu Zheng, Khimya Khetarpal … Will DabneyNeurIPS
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  3. 2024
    No Representation, No Trust: Connecting Representation, Collapse, and Trust Issues in PPOSkander Moalla, A. Miele, Pyatko, Daniil … Çağlar GülçehreNeurIPS
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  4. 2023
    Learning to Modulate pre-trained Models in RLThomas Schmied, Markus Hofmarcher, Fabian Paischer … Sepp HochreiterNeurIPS
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  5. 2023
    The Tunnel Effect: Building Data Representations in Deep Neural NetworksWojciech Masarczyk, Mateusz Ostaszewski, Ehsan Imani … T. P. TrzcinskiNeurIPS
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  6. 2023
    Deep Reinforcement Learning with Plasticity InjectionEvgenii Nikishin, Junhyuk Oh, Georg Ostrovski … André Sales BarretoNeurIPS
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  7. 2022
    Disentangling Transfer in Continual Reinforcement LearningMaciej Wołczyk, Michał Zając, Razvan Pascanu … Piotr MiłośNeurIPS
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  8. 2021
    Powerpropagation: A sparsity inducing weight reparameterisationJonathan Schwarz, Siddhant M. Jayakumar, Razvan Pascanu … Yee Whye TehNeurIPS · Google (United States)
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  9. 2021
    Continual World: A Robotic Benchmark For Continual Reinforcement LearningMaciej Wołczyk, Michał Zając, Razvan Pascanu … Piotr MiłośNeurIPS · Jagiellonian University · Google (United States)
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  10. 2020
    Understanding the Role of Training Regimes in Continual LearningSeyed Iman Mirzadeh, Mehrdad Farajtabar, Razvan Pascanu, Hassan GhasemzadehNeurIPS
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  11. 2019
    Continual Unsupervised Representation LearningDushyant Rao, Francesco Visin, Andrei Rusu … Raia HadsellNeurIPS · Carnegie Mellon University · Google (United States) · +2
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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. By default it shows the papers we have a reason to trust: published at a venue like NeurIPS, ICML, ICLR, CVPR or TPAMI, or written by someone who has published there, or cited a few hundred times. The rest are one click away under “All papers”. 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.