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.

7 papers of 6,984Sort Recent · Most cited
  1. 2023
    Design principles for lifelong learning AI acceleratorsDhireesha Kudithipudi, Anurag Daram, Abdullah M. Zyarah … Benjamin R. EpsteinNature Electronics · The University of Texas at San Antonio · Sandia National Laboratories · +6
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  2. 2023
    Improving Performance in Continual Learning Tasks using Bio-Inspired ArchitecturesSandeep Madireddy, Ángel Yanguas-Gil, Prasanna BalaprakashCoLLAs
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  3. 2023
    A Domain-Agnostic Approach for Characterization of Lifelong Learning SystemsMegan M. Baker, Alexander New, Mario Aguilar-Simon … Gautam K. VallabhaNeural Networks · Johns Hopkins University Applied Physics Laboratory · Teledyne Technologies (United States) · +13
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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. 2020
    Exploring Neuromodulation for Dynamic LearningAnurag Daram, Ángel Yanguas-Gil, Dhireesha KudithipudiFrontiers · The University of Texas at San Antonio · Argonne National Laboratory
  6. 2020
    Multilayer Neuromodulated Architectures for Memory-Constrained Online Continual LearningSandeep Madireddy, Ángel Yanguas-Gil, Prasanna BalaprakasharXiv · Argonne National Laboratory
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  7. 2019
    Task-Based Neuromodulation Architecture for Lifelong LearningAnurag Daram, Dhireesha Kudithipudi, Ángel Yanguas-GilInternational Symposium on Quality Electronic Design (ISQED) · Rochester Institute of Technology · Argonne National Laboratory
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.