35 citations · 35 across the 1 of their papers we have counts for
6 papers
Advocating for Multiple Defense Strategies against Adversarial Examples
Alexandre Araujo, Laurent Meunier, Rafael Pinot +1
It has been empirically observed that defense mechanisms designed to protect neural networks against adversarial examples offer poor performance against adve…
On Lipschitz Regularization of Convolutional Layers using Toeplitz Matrix Theory
Alexandre Araujo, Benjamin Negrevergne, Yann Chevaleyre +1
This paper tackles the problem of Lipschitz regularization of Convolutional Neural Networks. Lipschitz regularity is now established as a key property of modern deep learning with…
Robust Neural Networks using Randomized Adversarial Training
Alexandre Araujo, Laurent Meunier, Rafael Pinot +1
This paper tackles the problem of defending a neural network against adversarial attacks crafted with different norms (in particular and bounded adversarial…
Theoretical evidence for adversarial robustness through randomization
Rafael Pinot, Laurent Meunier, Alexandre Araujo +4
This paper investigates the theory of robustness against adversarial attacks. It focuses on the family of randomization techniques that consist in injecting noise in the network at…
Understanding and Training Deep Diagonal Circulant Neural Networks
Alexandre Araujo, Benjamin Negrevergne, Yann Chevaleyre +1
In this paper, we study deep diagonal circulant neural networks, that is deep neural networks in which weight matrices are the product of diagonal and circulant ones. Besides makin…
Training compact deep learning models for video classification using circulant matrices
Alexandre Araujo, Benjamin Negrevergne, Yann Chevaleyre +1
In real world scenarios, model accuracy is hardly the only factor to consider. Large models consume more memory and are computationally more intensive, which makes them difficult t…