10 citations · 43 across the 15 of their papers we have counts for
15 papers
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…
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…
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…
ARPEGE Cloud Cover Forecast Post-Processing with Convolutional Neural Network
Florian Dupuy, Olivier Mestre, Mathieu Serrurier +7
Cloud cover is crucial information for many applications such as planning land observation missions from space. It remains however a challenging variable to forecast, and Numerical…
Surrogate Models for Rainfall Nowcasting
Naty Citlali Cabrera-Gutiérrez, Hadrien Godé, Jean-Christophe Jouhaud +8
Nowcasting (or short-term weather forecasting) is particularly important in the case of extreme events as it helps prevent human losses. Many of our activities, however, also depen…
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…
Dyslexia and Dysgraphia prediction: A new machine learning approach
Gilles Richard, Mathieu Serrurier
Learning disabilities like dysgraphia, dyslexia, dyspraxia, etc. interfere with academic achievements but have also long terms consequences beyond the academic time. It is widely a…
Estimation of conditional mixture Weibull distribution with right-censored data using neural network for time-to-event analysis
Achraf Bennis, Sandrine Mouysset, Mathieu Serrurier
In this paper, we consider survival analysis with right-censored data which is a common situation in predictive maintenance and health field. We propose a model based on the estima…
From Shallow to Deep Interactions Between Knowledge Representation, Reasoning and Machine Learning (Kay R. Amel group)
Zied Bouraoui, Antoine Cornuéjols, Thierry Denœux +9
This paper proposes a tentative and original survey of meeting points between Knowledge Representation and Reasoning (KRR) and Machine Learning (ML), two areas which have been deve…
Learning Disentangled Representations via Mutual Information Estimation
Eduardo Hugo Sanchez, Mathieu Serrurier, Mathias Ortner
In this paper, we investigate the problem of learning disentangled representations. Given a pair of images sharing some attributes, we aim to create a low-dimensional representatio…
Learning Disentangled Representations of Satellite Image Time Series
Eduardo Sanchez, Mathieu Serrurier, Mathias Ortner
In this paper, we investigate how to learn a suitable representation of satellite image time series in an unsupervised manner by leveraging large amounts of unlabeled data. Additio…
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…