36 citations · 70 across the 8 of their papers we have counts for
9 papers
Bounding Box Anomaly Scoring for simple and efficient Out-of-Distribution detection
Mohamed Bahi Yahiaoui, Geoffrey Daniel, Loïc Giraldi +2
Out-of-distribution (OOD) detection aims to identify inputs that differ from the training distribution in order to reduce unreliable predictions by deep neural networks. Among post…
Ensemble Data Assimilation for Particle-based Methods
Marius Duvillard, Loïc Giraldi, Olivier Le Maître
This study presents a novel approach to applying data assimilation techniques for particle-based simulations using the Ensemble Kalman Filter. While data assimilation methods have…
A Gaussian process and linear-based framework for computing cut distributions in modular Bayesian calibration of two chained computer models
Oumar Baldé, Guillaume Damblin, Amandine Marrel +2
Computer models are widely used in science and engineering to simulate complex systems. However, these models are affected by several sources of uncertainty, which may limit their…
Conditional Latent Block Model: a Multivariate Time Series Clustering Approach for Autonomous Driving Validation
Etienne Goffinet, Anthony Coutant, Mustapha Lebbah +2
Autonomous driving systems validation remains one of the biggest challenges car manufacturers must tackle in order to provide safe driverless cars. The high complexity stems from s…
Optimal projection of observations in a Bayesian setting
Loïc Giraldi, Olivier P. Le Maître, Ibrahim Hoteit +1
Optimal dimensionality reduction methods are proposed for the Bayesian inference of a Gaussian linear model with additive noise in presence of overabundant data. Three different op…
Weakly intrusive low-rank approximation method for nonlinear parameter-dependent equations
Loic Giraldi, Anthony Nouy
This paper presents a weakly intrusive strategy for computing a low-rank approximation of the solution of a system of nonlinear parameter-dependent equations. The proposed strategy…
To be or not to be intrusive? The solution of parametric and stochastic equations --- Proper Generalized Decomposition
Loïc Giraldi, Dishi Liu, Hermann G. Matthies +1
A numerical method is proposed to compute a low-rank Galerkin approximation to the solution of a parametric or stochastic equation in a non-intrusive fashion. The considered nonlin…
To be or not to be intrusive? The solution of parametric and stochastic equations - the "plain vanilla" Galerkin case
Loïc Giraldi, Alexander Litvinenko, Dishi Liu +2
In parametric equations - stochastic equations are a special case - one may want to approximate the solution such that it is easy to evaluate its dependence of the parameters. Inte…
Low-rank approximate inverse for preconditioning tensor-structured linear systems
Loic Giraldi, Anthony Nouy, Gregory Legrain
In this paper, we propose an algorithm for the construction of low-rank approximations of the inverse of an operator given in low-rank tensor format. The construction relies on an…