45 citations · 99 across the 8 of their papers we have counts for
9 papers
Near-Linear Time Projection onto the Ball; Application to Sparse Autoencoders
Guillaume Perez, Laurent Condat, Michel Barlaud
Looking for sparsity is nowadays crucial to speed up the training of large-scale neural networks. Projections onto the and are among the most efficie…
Learning sparse auto-encoders for green AI image coding
Cyprien Gille, Frédéric Guyard, Marc Antonini +1
Recently, convolutional auto-encoders (CAE) were introduced for image coding. They achieved performance improvements over the state-of-the-art JPEG2000 method. However, these perfo…
Semi-supervised classification using a supervised autoencoder for biomedical applications
Cyprien Gille, Frederic Guyard, Michel Barlaud
In this paper we present a new approach to solve semi-supervised classification tasks for biomedical applications, involving a supervised autoencoder network. We create a network a…
Efficient Projection Algorithms onto the Weighted l1 Ball
Guillaume Perez, Sebastian Ament, Carla Gomes +1
Projected gradient descent has been proved efficient in many optimization and machine learning problems. The weighted ball has been shown effective in sparse system identi…
Robust supervised classification and feature selection using a primal-dual method
Michel Barlaud, Antonin Chambolle, Jean-Baptiste Caillau
This paper deals with supervised classification and feature selection in high dimensional space. A classical approach is to project data on a low dimensional space and classify by…
Clustering with feature selection using alternating minimization, Application to computational biology
Cyprien Gilet, Marie Deprez, Jean-Baptiste Caillau +1
This paper deals with unsupervised clustering with feature selection. The problem is to estimate both labels and a sparse projection matrix of weights. To address this combinatoria…