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.

9 papers of 8,653Sort Recent · Most cited
  1. 2026PDF ↗
  2. 2025PDF ↗
  3. 2024
    Reset It and Forget It: Relearning Last-Layer Weights Improves Continual and Transfer LearningL. Frati, Neil Traft, Jeff Clune, Nick CheneyFrontiers · University of Vermont · Canadian Institute for Advanced Research · +2
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  4. 2022
    Biological underpinnings for lifelong learning machinesDhireesha Kudithipudi, Mario Aguilar-Simon, Jonathan Babb … Hava T. SiegelmannNature Machine Intelligence · The University of Texas at San Antonio · Intelligent Systems Research (United States) · +24
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  5. 2021
    Continual learning under domain transfer with sparse synaptic burstingShawn Beaulieu, Jeff Clune, Nick CheneyarXiv · University of Vermont
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  6. 2019PDF ↗
  7. 2018
    Differentiable plasticity: training plastic neural networks with backpropagationThomas Miconi, Jeff Clune, Kenneth O. StanleyICML · Neurosciences Institute · University of Wyoming · +1
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  8. 2017
    Diffusion-based neuromodulation can eliminate catastrophic forgetting in simple neural networksRoby Velez, Jeff ClunePLOS · University of Wyoming · Uber AI (United States)
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  9. 2015
    Neural Modularity Helps Organisms Evolve to Learn New Skills without Forgetting Old SkillsKai Olav Ellefsen, Jean-Baptiste Mouret, Jeff ClunePLOS · Norwegian University of Science and Technology · Centre National de la Recherche Scientifique · +3
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.