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.

8 papers of 8,653Sort Recent · Most cited
  1. 2024
    Continual Learning and Catastrophic ForgettingGido M. van de Ven, Nicholas Soures, Dhireesha KudithipudiElsevier · KU Leuven · The University of Texas at San Antonio
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  2. 2024
    Probabilistic metaplasticity for continual learning with memristors in spiking networksFatima Tuz Zohora, Vedant Karia, Nicholas Soures, Dhireesha KudithipudiScientific Reports · The University of Texas at San Antonio
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  3. 2024
    TACOS: Task Agnostic Continual Learning in Spiking Neural NetworksNicholas Soures, Peter Helfer, Anurag Daram … Dhireesha KudithipudiarXiv
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  4. 2024
    Advancing Neuro-Inspired Lifelong Learning for Edge with Co-DesignNicholas Soures, Vedant Karia, Dhireesha KudithipudiAAAI · The University of Texas at San Antonio
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  5. 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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  6. 2022
    SCOLAR: A Spiking Digital Accelerator with Dual Fixed Point for Continual LearningVedant Karia, Fatima Tuz Zohora, Nicholas Soures, Dhireesha KudithipudiIEEE International Symposium on Circuits and Systems (ISCAS) · The University of Texas at San Antonio
  7. 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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  8. 2020
    Metaplasticity in Multistate Memristor Synaptic NetworksFatima Tuz Zohora, Abdullah M. Zyarah, Nicholas Soures, Dhireesha KudithipudiInternational Symposium on Circuits and Systems · The University of Texas at San Antonio · Rochester Institute of Technology
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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.