Related work

The foundational work on continual learning, 1988 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

5 papers of 6,984Sort Recent · Most cited
  1. 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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  2. 2021
    A good body is all you need: avoiding catastrophic interference via agent architecture searchJoshua Powers, Ryan Grindle, L. Frati, Josh BongardarXiv · University of Vermont
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  3. 2020
    Morphology dictates learnability in neural controllersJoshua Powers, Ryan Grindle, Sam Kriegman … Josh BongardThe 2020 Conference on Artificial Life · University of Vermont
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  4. 2018
    Embodiment can combat catastrophic forgettingJoshua Powers, Sam Kriegman, Josh BongardGenetic and Evolutionary Computation Conference Companion · University of Vermont
  5. 2018
    The effects of morphology and fitness on catastrophic interferenceJoshua Powers, Sam Kriegman, Josh BongardThe 2018 Conference on Artificial Life · University of Vermont · Morpho (United States)
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 lists only papers we have a reason to trust: published at a venue like NeurIPS, ICML, ICLR, CVPR or TPAMI, or led by someone who has published there, or cited a few hundred times. Preprints that later get accepted, and authors who later publish at those venues, are picked up by the weekly run. 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.