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Conference papers

Projections aléatoires pour l'apprentissage compressif

Abstract : Compressive learning is a framework to drastically compress the volume of large training collections while preserving the information needed to learn. Guided by unsupervised learning examples, we survey the involved tools, the existing theoretical guarantees both in terms of information preservation and of privacy preservation, and conclude with some open problems.
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https://hal.inria.fr/hal-02154803
Contributor : Antoine Chatalic Connect in order to contact the contributor
Submitted on : Thursday, June 13, 2019 - 9:05:04 AM
Last modification on : Friday, April 8, 2022 - 4:04:02 PM

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  • HAL Id : hal-02154803, version 1

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Antoine Chatalic, Nicolas Keriven, Rémi Gribonval. Projections aléatoires pour l'apprentissage compressif. GRETSI 2019 − XXVIIème Colloque francophone de traitement du signal et des images, Aug 2019, Lille, France. pp.1-4. ⟨hal-02154803⟩

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