Related work

The foundational work on continual learning, 1959 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

4 papers of 11,817Sort Recent · Most cited
  1. 2020
    Memory-Latency-Accuracy Trade-Offs for Continual Learning on a RISC-V Extreme-Edge NodeLeonardo Ravaglia, Manuele Rusci, Alessandro Capotondi … Luca BeniniIEEE Workshop on Signal Processing Systems · University of Bologna · University of Modena and Reggio Emilia · +1
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  2. 2020
    Continual Learning in Recurrent Neural Networks with HypernetworksBenjamin Ehret, Christian Henning, Maria R. Cervera … Benjamin F. GrewearXiv · ETH Zurich
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  3. 2020
    Coresets via Bilevel Optimization for Continual Learning and StreamingZalán Borsos, Mojmír Mutný, Andreas KrauseNeurIPS · ETH Zurich
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  4. 2020
    Reparameterizing Convolutions for Incremental Multi-Task Learning without Task InterferenceMenelaos Kanakis, David Brüggemann, Suman Saha … Luc Van GoolSpringer LNCS · ETH Zurich · KU Leuven
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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. 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.