18 citations · 27 across the 13 of their papers we have counts for
14 papers
Muon Is Theoretically Wrong For Convolutions, But Empirically Effective
Thibaut Boissin, Thomas Massena, Mathieu Serrurier +1
Muon, an optimizer known for its efficiency, has a clear interpretation for matrix-valued updates, but convolutional kernels are stored as four-dimensional tensors. Standard implem…
LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks
Arthur Chiron, Franck Mamalet, Thomas Massena +2
While accuracy, robustness, and calibration are all essential for reliable neural networks, they are often studied separately; developing models that satisfy all three simultaneous…
LipSSD: Lipschitz-Constrained Single-Shot Detection for Adversarially Robust Object Detection
Vincent Lébé, Yannick Prudent, Corentin Friedrich +3
Object detectors have many applications in safety-critical systems, but they are known to be sensitive to worst-case perturbations such as adversarial attacks, which limits their a…
Orthogonium : A Unified, Efficient Library of Orthogonal and 1-Lipschitz Building Blocks
Thibaut Boissin, Franck Mamalet, Valentin Lafargue +1
Orthogonal and 1-Lipschitz neural network layers are essential building blocks in robust deep learning architectures, crucial for certified adversarial robustness, stable generativ…
Fast and Flexible Robustness Certificates for Semantic Segmentation
Thomas Massena, Corentin Friedrich, Franck Mamalet +1
Deep Neural Networks are vulnerable to small perturbations that can drastically alter their predictions for perceptually unchanged inputs. The literature on adversarially robust De…
Turbo-Muon: Almost-Orthogonal Pre-Conditioning for Fast Muon Updates
Thibaut Boissin, Thomas Massena, Franck Mamalet +1
Orthogonality-based optimizers, such as Muon, have recently shown strong performance across large-scale training and community-driven efficiency challenges. However, these methods…
Efficient Robust Conformal Prediction via Lipschitz-Bounded Networks
Thomas Massena, Léo andéol, Thibaut Boissin +4
Conformal Prediction (CP) has proven to be an effective post-hoc method for improving the trustworthiness of neural networks by providing prediction sets with finite-sample guarant…
An Adaptive Orthogonal Convolution Scheme for Efficient and Flexible CNN Architectures
Thibaut Boissin, Franck Mamalet, Thomas Fel +3
Orthogonal convolutional layers are valuable components in multiple areas of machine learning, such as adversarial robustness, normalizing flows, GANs, and Lipschitz-constrained mo…
DP-SGD Without Clipping: The Lipschitz Neural Network Way
Louis Bethune, Thomas Massena, Thibaut Boissin +6
State-of-the-art approaches for training Differentially Private (DP) Deep Neural Networks (DNN) face difficulties to estimate tight bounds on the sensitivity of the network's layer…
On the explainable properties of 1-Lipschitz Neural Networks: An Optimal Transport Perspective
Mathieu Serrurier, Franck Mamalet, Thomas Fel +2
Input gradients have a pivotal role in a variety of applications, including adversarial attack algorithms for evaluating model robustness, explainable AI techniques for generating…
Existence, Stability and Scalability of Orthogonal Convolutional Neural Networks
El Mehdi Achour, François Malgouyres, Franck Mamalet
Imposing orthogonality on the layers of neural networks is known to facilitate the learning by limiting the exploding/vanishing of the gradient; decorrelate the features; improve t…
Pay attention to your loss: understanding misconceptions about 1-Lipschitz neural networks
Louis Béthune, Thibaut Boissin, Mathieu Serrurier +3
Lipschitz constrained networks have gathered considerable attention in the deep learning community, with usages ranging from Wasserstein distance estimation to the training of cert…
White Paper Machine Learning in Certified Systems
Hervé Delseny, Christophe Gabreau, Adrien Gauffriau +25
Machine Learning (ML) seems to be one of the most promising solution to automate partially or completely some of the complex tasks currently realized by humans, such as driving veh…
Achieving robustness in classification using optimal transport with hinge regularization
Mathieu Serrurier, Franck Mamalet, Alberto González-Sanz +3
Adversarial examples have pointed out Deep Neural Networks vulnerability to small local noise. It has been shown that constraining their Lipschitz constant should enhance robustnes…