Zobrazeno 1 - 10
of 4 233
pro vyhledávání: '"P, Pouliquen"'
Autor:
Lemoine, Antoine, Corre, Brieg Le, Morice, Lise, Harouri, Abdelmounaïm, Gratiet, Luc Le, Beaudoin, Grégoire, Pouliquen, Julie Le, Tavernier, Karine, Grisard, Arnaud, Combrié, Sylvain, Gérard, Bruno, Cornet, Charles, Dumeige, Yannick, Pantzas, Konstantinos, Sagnes, Isabelle, Léger, Yoan
Achieving high conversion efficiencies in second-order nonlinear optical processes is a key challenge in integrated photonics for both classical and quantum applications. This paper presents the first demonstration of Transverse Orientation-Patterned
Externí odkaz:
http://arxiv.org/abs/2412.15754
Estimating matrices in the symmetric positive-definite (SPD) cone is of interest for many applications ranging from computer vision to graph learning. While there exist various convex optimization-based estimators, they remain limited in expressivity
Externí odkaz:
http://arxiv.org/abs/2406.09023
We propose a method to remotely verify the authenticity of Optically Variable Devices (OVDs), often referred to as ``holograms'', in identity documents. Our method processes video clips captured with smartphones under common lighting conditions, and
Externí odkaz:
http://arxiv.org/abs/2404.17253
Autor:
Caroline Pouliquen
Publikováno v:
Mondes du Tourisme, Vol 11 (2015)
Externí odkaz:
https://doaj.org/article/10d0b1abfd8441658bfacc1854a167e8
This paper presents an end-to-end solution for the creation of fully automated conference meeting transcripts and their machine translations into various languages. This tool has been developed at the World Intellectual Property Organization (WIPO) u
Externí odkaz:
http://arxiv.org/abs/2309.15609
Publikováno v:
GRETSI 2023-XXIX{\`e}me Colloque Francophone de Traitement du Signal et des Images
We provide a framework and algorithm for tuning the hyperparameters of the Graphical Lasso via a bilevel optimization problem solved with a first-order method. In particular, we derive the Jacobian of the Graphical Lasso solution with respect to its
Externí odkaz:
http://arxiv.org/abs/2307.02130
Akademický článek
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Autor:
Gonon, Antoine, Zheng, Léon, Lalanne, Clément, Le, Quoc-Tung, Lauga, Guillaume, Pouliquen, Can
This article measures how sparsity can make neural networks more robust to membership inference attacks. The obtained empirical results show that sparsity improves the privacy of the network, while preserving comparable performances on the task at ha
Externí odkaz:
http://arxiv.org/abs/2304.10553
Autor:
Gonon, Antoine, Zheng, Léon, Lalanne, Clément, Le, Quoc-Tung, Lauga, Guillaume, Pouliquen, Can
Sparse neural networks are mainly motivated by ressource efficiency since they use fewer parameters than their dense counterparts but still reach comparable accuracies. This article empirically investigates whether sparsity could also improve the pri
Externí odkaz:
http://arxiv.org/abs/2304.07234
Akademický článek
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