Confident Object Detection via Conformal Prediction and Conformal Risk Control: an Application to Railway Signaling

Autor: Andéol, Léo, Fel, Thomas, De Grancey, Florence, Mossina, Luca

Deploying deep learning models in real-world certified systems requires the ability to provide confidence estimates that accurately reflect their uncertainty. In this paper, we demonstrate the use of the conformal prediction framework to construct re

Externí odkaz: http://arxiv.org/abs/2304.06052

White Paper Machine Learning in Certified Systems

Autor: Delseny, Hervé, Gabreau, Christophe, Gauffriau, Adrien, Beaudouin, Bernard, Ponsolle, Ludovic, Alecu, Lucian, Bonnin, Hugues, Beltran, Brice, Duchel, Didier, Ginestet, Jean-Brice, Hervieu, Alexandre, Martinez, Ghilaine, Pasquet, Sylvain, Delmas, Kevin, Pagetti, Claire, Gabriel, Jean-Marc, Chapdelaine, Camille, Picard, Sylvaine, Damour, Mathieu, Cappi, Cyril, Gardès, Laurent, De Grancey, Florence, Jenn, Eric, Lefevre, Baptiste, Flandin, Gregory, Gerchinovitz, Sébastien, Mamalet, Franck, Albore, Alexandre

Machine Learning (ML) seems to be one of the most promising solution to automate partially or completely some of the complex tasks currently realized by humans, such as driving vehicles, recognizing voice, etc. It is also an opportunity to implement

Externí odkaz: http://arxiv.org/abs/2103.10529

Premiers états du $^{15}$F étudiés par diffusion élastique résonante

Autor: De Grancey, Florence

Cette thèse s'inscrit dans le cadre de l'étude des noyaux déficients en neutrons situés au delà de la drip-line proton. Afin d'étudier le noyau de 15F, une expérience de diffusion élastique résonante d'un faisceau d'ions radioactifs d'14O su

Externí odkaz: http://tel.archives-ouvertes.fr/tel-00448658
http://tel.archives-ouvertes.fr/docs/00/44/86/58/PDF/These-Grancey_GANIL.pdf

Object Detection With Probabilistic Guarantees: a Conformal Prediction Approach

Autor: de Grancey, Florence, Adam, Jean-Luc, Alecu, Lucian, Gerchinovitz, Sébastien, Mamalet, Franck, Vigouroux, David

Publikováno v: Fifth International Workshop on Artificial Intelligence Safety Engineering (WAISE 2022)
Fifth International Workshop on Artificial Intelligence Safety Engineering (WAISE 2022), Sep 2022, München, Germany

This preprint has not undergone peer review or any post-submission improvements or corrections. The Version of Record of this contribution will appear in SAFECOMP 2022, LNCS 13415 proceedings.; International audience; Providing reliable uncertainty q

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______4074::89a2c4b40b932e8508d5fff5c7feb152
https://hal.science/hal-03769683/document