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.

6 papers of 11,817Sort Recent · Most cited
  1. 2025
    Toward Sustainable Continual Learning: Detection and Knowledge Repurposing for Reoccurring TasksSijia Wang, Yoojin Choi, Junya Chen … Ricardo HenaoInternational Workshop on Machine Learning for Signal Pro…
  2. 2025
    Unseen Speaker and Language Adaptation for Lightweight Text-to-Speech with AdaptersAlessio Falai, Ziyao Zhang, Akos GangolyInternational Workshop on Machine Learning for Signal Pro…
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  3. 2025
    Tackling Distribution Shift in LLM via KILO: Knowledge-Instructed Learning for Continual AdaptationLing Muttakhiroh, Thomas FevensInternational Workshop on Machine Learning for Signal Pro…
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  4. 2025
    SAGE: Spliced-Audio Generated Data for Enhancing Foundational Models in Low-Resource Arabic-English Code-Switched Speech RecognitionMuhammad Umar Farooq, Oscar Saz-TorralbaInternational Workshop on Machine Learning for Signal Pro…
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  5. 2024
    Prediction of Imaging Data Based on Signal Processing Boosted by Neural Networks with Continual LearningVictor SanchezInternational Workshop on Machine Learning for Signal Pro…
  6. 2023
    Memory Replay For Continual Learning With Spiking Neural NetworksMichela Proietti, Alessio Ragno, Roberto CapobiancoInternational Workshop on Machine Learning for Signal Pro…
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.