10 citations · 43 across the 15 of their papers we have counts for
7 papers · 1 filter
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…
Robust One-Class Classification with Signed Distance Function using 1-Lipschitz Neural Networks
Louis Bethune, Paul Novello, Thibaut Boissin +4
We propose a new method, dubbed One Class Signed Distance Function (OCSDF), to perform One Class Classification (OCC) by provably learning the Signed Distance Function (SDF) to the…
Precipitaion Nowcasting using Deep Neural Network
Mohamed Chafik Bakkay, Mathieu Serrurier, Valentin Kivachuk Burda +8
Precipitation nowcasting is of great importance for weather forecast users, for activities ranging from outdoor activities and sports competitions to airport traffic management. In…
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…
A Hölderian backtracking method for min-max and min-min problems
Jérôme Bolte, Lilian Glaudin, Edouard Pauwels +1
We present a new algorithm to solve min-max or min-min problems out of the convex world. We use rigidity assumptions, ubiquitous in learning, making our method applicable to many o…
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…
Predictive Interval Models for Non-parametric Regression
Mohammad Ghasemi Hamed, Mathieu Serrurier, Nicolas Durand
Having a regression model, we are interested in finding two-sided intervals that are guaranteed to contain at least a desired proportion of the conditional distribution of the resp…