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.

5 papers of 8,653Sort Recent · Most cited
  1. 2024
    Compressed Latent Replays for Lightweight Continual Learning on Spiking Neural NetworksAlberto Dequino, Alessio Carpegna, Davide Nadalini … Francesco ContiIEEE Computer Society Annual Symposium on VLSI (ISVLSI) · University of Bologna · Politecnico di Torino
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  2. 2024
    Structured Sparse Back-propagation for Lightweight On-Device Continual Learning on Microcontroller UnitsFrancesco Paissan, Davide Nadalini, Manuele Rusci … Elisabetta FarellaCVPR · Fondazione Bruno Kessler · Politecnico di Torino · +2
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  3. 2022
    ViT-LR: Pushing the Envelope for Transformer-Based on-Device Embedded Continual LearningAlberto Dequino, Francesco Conti, Luca BeniniIEEE 13th International Green and Sustainable Computing C… · University of Bologna
  4. 2021
    A TinyML Platform for On-Device Continual Learning With Quantized Latent ReplaysLeonardo Ravaglia, Manuele Rusci, Davide Nadalini … Luca BeniniIEEE Journal on Emerging and Selected Topics in Circuits… · University of Bologna · University of Modena and Reggio Emilia · +1
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  5. 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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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.