Vous êtes ici : Accueil » Kiosque » Annonce


Mot de passe : 

Mot de passe oublié ?
Détails d'identification oubliés ?


31 juillet 2017

Approximate Message Passing for HIgh-dimensional data analysis (AMPHI)

Catégorie : Post-doctorant

Salary: 2100 € / month (net)

Duration: 12 months (with possible extensions), starting end 2017

Place: Inria Parietal team (Paris-Saclay)

More details available at this link: https://team.inria.fr/parietal/files/2017/07/amphi_post_doc.pdf


Summary of the project

In many scientific fields, the data acquisition devices have benefited of hardware improvement to increase the resolution of the observed phenomena, leading to ever larger datasets. While the dimensionality has increased, the number of samples available is often limited, due to physical or financial limits. This is a problem when these data are processed with estimators that have a large sample complexity, such as multivariate statistical models. In that case it is very useful to rely on structured priors, so that the results reflect the state of knowledge on the phenomena of interest. The study of the human brain activity through neuroimaging belongs among these problems, with up to 10^6 features, yet a set of observations limited by cost and participant comfort.

We are missing fast estimators for multivariate models with structured priors , that furthermore provide statistical control on the solution. We want to join forces to design a new generation of inverse problem solvers that can take into account the complex structure of brain images and provide guarantees in the low-sample-complexity regime. To this end, we will first adapt alternating direction method of multipliers (ADMM) or Approximate Message Passing (AMP) methods to the brain mapping setting, using first simple convex priors regularizations. We will then consider more complex structured priors that control the variation of the learned image patterns in space and non-convex priors. Crucial gains are expected from the use of the EM algorithm for parameter setting. We will also examine the estimation of parametric and non-parametric confidence intervals about the estimates. AMPHI will design a reference inference toolbox released as a generic open source library. We expect a 3- to 10-fold improvement in CPU time with respect to current solutions, that will benefit to large-scale brain mapping investigations.

More details available at this link: https://team.inria.fr/parietal/files/2017/07/amphi_post_doc.pdf


Dans cette rubrique

(c) GdR 720 ISIS - CNRS - 2011-2015.