3 citations · 5 across the 4 of their papers we have counts for
3 papers · 1 filter
Explainable-by-design Semi-Supervised Representation Learning for COVID-19 Diagnosis from CT Imaging
Abel Díaz Berenguer, Hichem Sahli, Boris Joukovsky +37
Our motivating application is a real-world problem: COVID-19 classification from CT imaging, for which we present an explainable Deep Learning approach based on a semi-supervised c…
A Deep-Unfolded Reference-Based RPCA Network For Video Foreground-Background Separation
Huynh Van Luong, Boris Joukovsky, Yonina C. Eldar +1
Deep unfolded neural networks are designed by unrolling the iterations of optimization algorithms. They can be shown to achieve faster convergence and higher accuracy than their op…
Interpretable Deep Recurrent Neural Networks via Unfolding Reweighted - Minimization: Architecture Design and Generalization Analysis
Huynh Van Luong, Boris Joukovsky, Nikos Deligiannis
Deep unfolding methods---for example, the learned iterative shrinkage thresholding algorithm (LISTA)---design deep neural networks as learned variations of optimization methods. Th…