no code implementations • 20 Nov 2023 • Mounia Hamidouche, Eugeny Popko, Bassem Ouni
This work provides a comparative analysis illustrating how Deep Learning (DL) surpasses Machine Learning (ML) in addressing tasks within Internet of Things (IoT), such as attack classification and device-type identification.
no code implementations • 30 May 2022 • Mounia Hamidouche, Reda Bellafqira, Gwenolé Quellec, Gouenou Coatrieux
From a privacy perspective in our use case where a diabetic retinopathy classification model is given to partners that have at their disposal images along with patients' identifiers, inferring the membership status of a data sample can help to state if a patient has contributed or not to the training of the model.
no code implementations • 8 Oct 2021 • Carlos Lassance, Myriam Bontonou, Mounia Hamidouche, Bastien Pasdeloup, Lucas Drumetz, Vincent Gripon
This chapter is composed of four main parts: tools for visualizing intermediate layers in a DNN, denoising data representations, optimizing graph objective functions and regularizing the learning process.
no code implementations • 12 Jan 2021 • Mounia Hamidouche, Carlos Lassance, Yuqing Hu, Lucas Drumetz, Bastien Pasdeloup, Vincent Gripon
In machine learning, classifiers are typically susceptible to noise in the training data.
1 code implementation • 25 Nov 2020 • Carlos Lassance, Louis Béthune, Myriam Bontonou, Mounia Hamidouche, Vincent Gripon
Measuring the generalization performance of a Deep Neural Network (DNN) without relying on a validation set is a difficult task.