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NORIA SIGNED

Numerical Optimal tRansport for ImAging

Total Cost €

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EC-Contrib. €

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Partnership

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 NORIA project word cloud

Explore the words cloud of the NORIA project. It provides you a very rough idea of what is the project "NORIA" about.

niche    bottlenecks    practical    noria    favorably    codes    optimization    numerical    fast    texture    exploration    barely    school    computational    players    ot    opportunity    embodiment    transport    sciences    computer    clouds    outputs    consumes    parallelizable    restricted    confidential    broad    members    probability    substantial    ing    schemes    powerful    flagship    gradient    tool    suitable    data    optimal    spaces    theoretical    divergences    synthesis    sensitive    visual    bregman    entropic    routinely    computations    naively    reaching    imaging    distributions    algorithms    algorithmic    imag    graphics    quantization    compare    regularization    principles    intuitive    language    point    breakthroughs    mathematically    generation    vision    mathematical    metric    euclidean    hinders    color    framework    neuroimaging    manipulate    sense    geometric    time    standard    notably    material    distances    scripting    alternatives    flows    wealth    cortex    give    interfaced    metrics    provides    favorable    stochastic    science    theory    rely   

Project "NORIA" data sheet

The following table provides information about the project.

Coordinator
CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS 

Organization address
address: RUE MICHEL ANGE 3
city: PARIS
postcode: 75794
website: www.cnrs.fr

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country France [FR]
 Total cost 1˙996˙720 €
 EC max contribution 1˙996˙720 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2016-COG
 Funding Scheme ERC-COG
 Starting year 2017
 Duration (year-month-day) from 2017-10-01   to  2022-09-30

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS FR (PARIS) coordinator 1˙996˙720.00

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 Project objective

Optimal Transport (OT) theory provides a powerful framework to manipulate probability distributions using simple and intuitive geometric principles. OT distances compare favorably to all other alternatives, notably Euclidean metrics or information divergences, whose outputs are particularly sensitive to changes in quantization and are not suitable to compare point clouds. Because of these and many more favorable properties, OT should be a standard tool in imaging sciences where probability distributions are routinely used. However, at this time, OT is but a confidential tool restricted to niche applications. OT is barely used because it is complex mathematically, which hinders its dissemination in more applied fields, and because it consumes substantial computational resources when used naively. NORIA will address these two bottlenecks and develop the next generation of theoretical, numerical and algorithmic advances to enable large-scale optimal transport computations in imag- ing sciences. The algorithms developed by NORIA will rely on several mathematical breakthroughs: highly parallelizable entropic regularization schemes, Bregman stochastic optimization and gradient flows for metric spaces. They will be implemented using fast optimization codes that will be interfaced through a high-level, easy to use, scripting language. These algorithms will have far reaching applications in imaging sciences and data science in a broad sense. In particular, they will be used in three flagship applications: color and material processing in computer graphics, texture analysis and synthesis in computer vision, and exploration of the visual cortex in neuroimaging. NORIA’s members are key players in the European mathematical school of optimal transport, which is very strong. NORIA is the unique opportunity to give a computational and practical embodiment to this wealth of theoretical knowledge.

 Publications

year authors and title journal last update
List of publications.
2019 A. Genevay, L. Chizat, F. Bach, M. Cuturi, G. Peyré
Sample Complexity of Sinkhorn divergences
published pages: , ISSN: , DOI:
Proc AISTATS2019 2019-08-05
2018 Jingwei Liang, Jalal Fadili, Gabriel Peyré
Local linear convergence analysis of Primal–Dual splitting methods
published pages: 821-853, ISSN: 0233-1934, DOI: 10.1080/02331934.2018.1426584
Optimization 67/6 2019-08-06
2018 Lénaïc Chizat, Gabriel Peyré, Bernhard Schmitzer, François-Xavier Vialard
An Interpolating Distance Between Optimal Transport and Fisher–Rao Metrics
published pages: 1-44, ISSN: 1615-3375, DOI: 10.1007/s10208-016-9331-y
Foundations of Computational Mathematics 18/1 2019-08-06
2019 C. Poon, N. Keriven, G. Peyré
Support localization and the fisher metric for off-the-grid sparse regularization
published pages: , ISSN: , DOI:
Proc AISTATS 2019 2019-08-05
2018 Marco Cuturi, Gabriel Peyré
Semidual Regularized Optimal Transport
published pages: 941-965, ISSN: 0036-1445, DOI: 10.1137/18m1208654
SIAM Review 60/4 2019-08-06
2018 Lénaïc Chizat, Gabriel Peyré, Bernhard Schmitzer, François-Xavier Vialard
Unbalanced optimal transport: Dynamic and Kantorovich formulations
published pages: 3090-3123, ISSN: 0022-1236, DOI: 10.1016/j.jfa.2018.03.008
Journal of Functional Analysis 274/11 2019-08-06
2018 Gabriel Peyré, Marco Cuturi
Computational Optimal Transport
published pages: 355-206, ISSN: 1935-8237, DOI: 10.1561/2200000073
Foundations and Trends® in Machine Learning 11/5-6 2019-08-05
2019 Clarice Poon, Gabriel Peyré
MultiDimensional Sparse Super-Resolution
published pages: 1-44, ISSN: 0036-1410, DOI: 10.1137/17m1147822
SIAM Journal on Mathematical Analysis 51/1 2019-08-05
2018 Jalal Fadili, Jérôme Malick, Gabriel Peyré
Sensitivity Analysis for Mirror-Stratifiable Convex Functions
published pages: 2975-3000, ISSN: 1052-6234, DOI: 10.1137/17m113825x
SIAM Journal on Optimization 28/4 2019-08-06
2019 J. Fadili, G. Garrigos, J. Malick, G. Peyré
Model Consistency for Learning with Mirror-Stratifiable Regularizers
published pages: , ISSN: , DOI:
Proc AISTATS2019 2019-08-05

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