no code implementations • 13 Feb 2024 • Matthieu Nastorg, Jean-Marc Gratien, Thibault Faney, Michele Alessandro Bucci, Guillaume Charpiat, Marc Schoenauer
The proposed GNN-based preconditioner is used to enhance the efficiency of a Krylov method, resulting in a hybrid solver that can converge with any desired level of accuracy.
no code implementations • 26 Jun 2023 • Thibault Monsel, Onofrio Semeraro, Lionel Mathelin, Guillaume Charpiat
The developed framework is auto-differentiable and runs efficiently on multiple backends.
no code implementations • 6 Feb 2023 • Matthieu Nastorg, Michele Alessandro Bucci, Thibault Faney, Jean-Marc Gratien, Guillaume Charpiat, Marc Schoenauer
This paper presents $\Psi$-GNN, a novel Graph Neural Network (GNN) approach for solving the ubiquitous Poisson PDE problems with mixed boundary conditions.
no code implementations • 13 Jan 2023 • Loris Felardos, Jérôme Hénin, Guillaume Charpiat
Generating a Boltzmann distribution in high dimension has recently been achieved with Normalizing Flows, which enable fast and exact computation of the generated density, and thus unbiased estimation of expectations.
no code implementations • 21 Nov 2022 • Matthieu Nastorg, Marc Schoenauer, Guillaume Charpiat, Thibault Faney, Jean-Marc Gratien, Michele-Alessandro Bucci
This paper proposes a novel Machine Learning-based approach to solve a Poisson problem with mixed boundary conditions.
no code implementations • 6 Nov 2022 • Francesco Saverio Pezzicoli, Guillaume Charpiat, François P. Landes
The difficult problem of relating the static structure of glassy liquids and their dynamics is a good target for Machine Learning, an approach which excels at finding complex patterns hidden in data.
no code implementations • 12 Jul 2022 • Tamon Nakano, Alessandro Michele Bucci, Jean-Marc Gratien, Thibault Faney, Guillaume Charpiat
The volume of fluid (VoF) method is widely used in multi-phase flow simulations to track and locate the interface between two immiscible fluids.
no code implementations • 12 Jul 2022 • Antoine Fosset, Mohamed El-Mennaoui, Amine Rebei, Paul Calligaro, Elise Farge Di Maria, Hélène Nguyen-Ban, Francesca Rea, Marie-Charlotte Vallade, Elisabetta Vitullo, Christophe Zhang, Guillaume Charpiat, Mathieu Rosenbaum
The power of graph analysis enables us to provide an artwork recommendation system based on a combination of visual and contextual information from artworks and artists.
no code implementations • 17 May 2021 • Julien Girard-Satabin, Aymeric Varasse, Marc Schoenauer, Guillaume Charpiat, Zakaria Chihani
The impressive results of modern neural networks partly come from their non linear behaviour.
1 code implementation • NeurIPS 2019 • Guillaume Charpiat, Nicolas Girard, Loris Felardos, Yuliya Tarabalka
We first exhibit a multimodal image registration task, for which a neural network trained on a dataset with noisy labels reaches almost perfect accuracy, far beyond noise variance.
no code implementations • 1 Feb 2020 • Pierre Wolinski, Guillaume Charpiat, Yann Ollivier
We fully characterize the regularizers that can arise according to this procedure, and provide a systematic way to compute the prior corresponding to a given penalty.
no code implementations • 25 Nov 2019 • Julien Girard-Satabin, Guillaume Charpiat, Zakaria Chihani, Marc Schoenauer
We propose to take advantage of the simulators often used either to train machine learning models or to check them with statistical tests, a growing trend in industry.
1 code implementation • 23 Oct 2019 • Sophie Giffard-Roisin, Mo Yang, Guillaume Charpiat, Christina Kumler-Bonfanti, Balázs Kégl, Claire Monteleoni
The forecast of tropical cyclone trajectories is crucial for the protection of people and property.
1 code implementation • 12 Mar 2019 • Nicolas Girard, Guillaume Charpiat, Yuliya Tarabalka
In machine learning the best performance on a certain task is achieved by fully supervised methods when perfect ground truth labels are available.
no code implementations • 7 Sep 2018 • Hugo Richard, Ana Pinho, Bertrand Thirion, Guillaume Charpiat
The comparison of observed brain activity with the statistics generated by artificial intelligence systems is useful to probe brain functional organization under ecological conditions.
no code implementations • ECCV 2018 • Armand Zampieri, Guillaume Charpiat, Nicolas Girard, Yuliya Tarabalka
We tackle here the problem of multimodal image non-rigid registration, which is of prime importance in remote sensing and medical imaging.
no code implementations • 27 Feb 2018 • Armand Zampieri, Guillaume Charpiat, Yuliya Tarabalka
We tackle here the problem of multimodal image non-rigid registration, which is of prime importance in remote sensing and medical imaging.
no code implementations • 7 Nov 2016 • Emmanuel Maggiori, Yuliya Tarabalka, Guillaume Charpiat, Pierre Alliez
We establish the desired properties of an ideal semantic labeling CNN, and assess how those methods stand with regard to these properties.
no code implementations • 11 Aug 2016 • Emmanuel Maggiori, Guillaume Charpiat, Yuliya Tarabalka, Pierre Alliez
Instead, our goal is to directly learn the iterative process itself.
no code implementations • 28 Jul 2015 • Yann Ollivier, Corentin Tallec, Guillaume Charpiat
The evolution of this search direction is partly stochastic and is constructed in such a way to provide, at every time, an unbiased random estimate of the gradient of the loss function with respect to the parameters.