4 papers
Schur's Positive-Definite Network: Deep Learning in the SPD cone with structure
Can Pouliquen, Mathurin Massias, Titouan Vayer
Estimating matrices in the symmetric positive-definite (SPD) cone is of interest for many applications ranging from computer vision to graph learning. While there exist various con…
Implicit Differentiation for Hyperparameter Tuning the Weighted Graphical Lasso
Can Pouliquen, Paulo Gonçalves, Mathurin Massias +1
We provide a framework and algorithm for tuning the hyperparameters of the Graphical Lasso via a bilevel optimization problem solved with a first-order method. In particular, we de…
Sparsity in neural networks can improve their privacy
Antoine Gonon, Léon Zheng, Clément Lalanne +3
This article measures how sparsity can make neural networks more robust to membership inference attacks. The obtained empirical results show that sparsity improves the privacy of t…
Can sparsity improve the privacy of neural networks?
Antoine Gonon, Léon Zheng, Clément Lalanne +3
Sparse neural networks are mainly motivated by ressource efficiency since they use fewer parameters than their dense counterparts but still reach comparable accuracies. This articl…