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. 2025
    Towards Experience Replay for Class-Incremental Learning in Fully-Binary NetworksYanis Basso-Bert, Anca Molnos, Romain Lemaire … Antoine DupretACM Transactions · Commissariat à l'Énergie Atomique et aux Énergies Alternatives · CEA Grenoble · +3
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  2. 2024
    On Class-Incremental Learning for Fully Binarized Convolutional Neural NetworksYanis Basso-Bert, William Guicquéro, Anca Molnos … Antoine DupretIEEE International Symposium on Circuits and Systems (ISCAS) · Commissariat à l'Énergie Atomique et aux Énergies Alternatives · Laboratoire d'Électronique des Technologies de l'Information · +2
  3. 2023
    Synaptic metaplasticity with multi-level memristive devicesS. D’Agostino, Filippo Moro, Tifenn Hirtzlin … Elisa VianelloInternational Conference on Artificial Intelligence Circu… · Commissariat à l'Énergie Atomique et aux Énergies Alternatives · Laboratoire d'Électronique des Technologies de l'Information · +4
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  4. 2021
    Combining Accuracy and Plasticity in Convolutional Neural Networks Based on Resistive Memory Arrays for Autonomous LearningS. Bianchi, Irene Muñoz-Martín, Erika Covi … Daniele IelminiIEEE Journal on Exploratory Solid-State Computational Dev… · Politecnico di Milano · NaMLab (Germany) · +4
  5. 2020
    A SiOx, RRAM-based hardware with spike frequency adaptation for power-saving continual learning in convolutional neural networksIrene Muñoz-Martín, S. Bianchi, Erika Covi … Daniele IelminiIEEE Symposium on VLSI Technology · Politecnico di Milano · Commissariat à l'Énergie Atomique et aux Énergies Alternatives · +2
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.