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.

15 papers of 8,653Sort Recent · Most cited
  1. 2025
    Thousand-Brains Systems: Sensorimotor Intelligence for Rapid, Robust Learning and InferenceNiels Leadholm, Viviane Clay, Scott G. Knudstrup … Jeff HawkinsNeural Computation
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  2. 2025
    Improving Recall in Sparse Associative Memories That Use NeurogenesisKaty Warr, Jonathon Hare, David ThomasNeural Computation · University of Southampton
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  3. 2025
    Sequential Learning in the Dense Associative MemoryH. A. McAlister, Anthony Robins, Lech SzymanskiNeural Computation
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  4. 2024
    Hebbian Descent: A Unified View on Log-Likelihood LearningJan Melchior, Robin Schiewer, Laurenz WiskottNeural Computation · Ruhr University Bochum
  5. 2023
    Reducing Catastrophic Forgetting With Associative Learning: A Lesson From Fruit FliesYang Shen, Sanjoy Dasgupta, Saket NavlakhaNeural Computation
  6. 2023
    Dynamic Consolidation for Continual LearningHang Li, Chen Ma, Xi Chen, Xue LiuNeural Computation · McGill University · City University of Hong Kong
  7. 2021
    Replay in Deep Learning: Current Approaches and Missing Biological ElementsTyler L. Hayes, Giri P. Krishnan, Maxim Bazhenov … Christopher KananNeural Computation · Rochester Institute of Technology · University of California San Diego · +5
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  8. 2021
    Task-Agnostic Continual Learning Using Online Variational Bayes With Fixed-Point UpdatesChen Zeno, Itay Golan, Elad Hoffer, Daniel SoudryNeural Computation · Technion – Israel Institute of Technology · Itamar Medical (Israel)
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  9. 2021
    Artificial Neural Variability for Deep Learning: On Overfitting, Noise Memorization, and Catastrophic ForgettingZeke Xie, Fengxiang He, Shaopeng Fu … Masashi SugiyamaNeural Computation · RIKEN Center for Advanced Intelligence Project · The University of Tokyo · +1
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  10. 2020
    Toward Training Recurrent Neural Networks for Lifelong LearningShagun Sodhani, Sarath Chandar, Yoshua BengioNeural Computation · Université de Montréal
  11. 2019
    Adversarial Feature Alignment: Avoid Catastrophic Forgetting in Incremental Task Lifelong LearningXin Yao, Tianchi Huang, Chenglei Wu … Lifeng SunNeural Computation · Tsinghua University
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  12. 2018
    On Training Recurrent Neural Networks for Lifelong LearningShagun Sodhani, Sarath Chandar, Yoshua BengioNeural Computation
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  13. 2016
  14. 2011
  15. 2002
    Using Noise to Compute Error Surfaces in Connectionist Networks: A Novel Means of Reducing Catastrophic ForgettingRobert M. French, Nick ChaterNeural Computation · University of Liège · University of Warwick
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.