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Publications associées (25)

Theoretical foundations of forward feature selection methods based on mutual information

Francisco Santos Paredes Quartin de Macedo

Feature selection problems arise in a variety of applications, such as microarray analysis, clinical prediction, text categorization, image classification and face recognition, multi-label learning, and classification of internet traffic. Among the various ...

ELSEVIER SCIENCE BV2019

Multimodal person recognition in audio-visual streams

Do Hoang Nam Le

Multimedia databases are growing rapidly in size in the digital age. To increase the value of these data and to enhance the user experience, there is a need to make these videos searchable through automatic indexing. Because people appearing and talking in ...

EPFL2019

A Geometry-Inspired Decision-Based Attack

Pascal Frossard, Seyed Mohsen Moosavi Dezfooli, Yujia Liu

Deep neural networks have recently achieved tremendous success in image classification. Recent studies have however shown that they are easily misled into incorrect classification decisions by adversarial examples. Adversaries can even craft attacks by que ...

IEEE COMPUTER SOC2019

Deep neural networks for plasma tomography with applications to JET and COMPASS

Olivier Sauter, Ambrogio Fasoli, Yiming Li, Basil Duval, Jonathan Graves, Duccio Testa, Patrick Blanchard, Henri Weisen, Federico Nespoli, Alessandro Pau, Federico Alberto Alfredo Felici, Dalziel Joseph Wilson, Hamish William Patten, Cristian Sommariva, Antoine Pierre Emmanuel Alexis Merle, Samuel Lanthaler, Haomin Sun, Michele Marin, Richard Pitts, Yann Camenen, Jan Horacek, Javier García Hernández, Marco Wischmeier, Nicola Vianello, Mikhail Maslov, Yao Zhou, Davide Galassi, Antonio José Pereira de Figueiredo, Emiliano Fable, Francesca Maria Poli, Daniele Brunetti, Anna Teplukhina, Alberto Mariani, Kenji Tanaka, Bernhard Sieglin, Otto Asunta, Gergely Papp, Leonardo Pigatto

Convolutional neural networks (CNNs) have found applications in many image processing tasks, such as feature extraction, image classification, and object recognition. It has also been shown that the inverse of CNNs, so-called deconvolutional neural network ...

2019

Learning to See through Reflections

Sabine Süsstrunk

"Pictures of objects behind a glass are difficult to interpret" "and understand due to the superposition of two real images: a reflection layer and a background layer. Separation of these two layers is challenging due to the ambiguities in as- signing text ...

IEEE2018

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