Dans le contexte de la thermomécanique convexe, nous proposons des outils basés sur le concept de divergence de Bregman, une notion introduite dans les années 1960 et utilisée dans d’autres domaines aussi bien en apprentissage qu’en optimisation. Cette étude est motivée par le besoin de "mesures de dissimilarité" entre des champs physiquement contraints, champs qui sont utilisés à la fois dans les algorithmes traditionnels, les méthodes d'analyse et les applications ou les modélisations basées sur les données. Nous donnons également une caractérisation des divergences de Bregman symétriques à travers leur fonction génératrice qui ne peut être qu'une fonction quadratique. Certaines propriétés de la divergence de Bregman et du concept introdu...
In image and signal processing, the beta-divergence is well known as a similarity measure between tw...
Here we examine some connections between the notions of generalized arithmetic means, geodesics, Lag...
Nonparametric convex regression has been extensively studied over the last two decades. It has been ...
Dans le contexte de la thermomécanique convexe, nous proposons des outils basés sur le concept de di...
Bregman divergences play a central role in the design and analysis of a range of machine learning al...
Dans ce travail, nous développons et examinons deux nouveaux algorithmes d'éclatement du premier ord...
En apprentissage statistique et traitement du signal, de nombreuses tâches se formulent sous la form...
MI: Global COE Program Education-and-Research Hub for Mathematics-for-IndustryグローバルCOEプログラム「マス・フォア・イ...
This dissertation has mainly focused on the development of statistical theory, methodology, and appl...
The difference-of-convex (DC) algorithm is a conceptually simple method for the minimization of (non...
Bregman divergences generalize measures such as the squared Euclidean distance and the KL divergenc...
We introduce a class of discrete divergences on sets (equivalently binary vectors) that we call the ...
The Bregman and Total Bregman divergences are useful for determining the similarity of complex data...
summary:The problem to maximize the information divergence from an exponential family is generalized...
En este trabajo se desarrollan ciertas propiedades relativas a la convexidad de la M-divergencia en ...
In image and signal processing, the beta-divergence is well known as a similarity measure between tw...
Here we examine some connections between the notions of generalized arithmetic means, geodesics, Lag...
Nonparametric convex regression has been extensively studied over the last two decades. It has been ...
Dans le contexte de la thermomécanique convexe, nous proposons des outils basés sur le concept de di...
Bregman divergences play a central role in the design and analysis of a range of machine learning al...
Dans ce travail, nous développons et examinons deux nouveaux algorithmes d'éclatement du premier ord...
En apprentissage statistique et traitement du signal, de nombreuses tâches se formulent sous la form...
MI: Global COE Program Education-and-Research Hub for Mathematics-for-IndustryグローバルCOEプログラム「マス・フォア・イ...
This dissertation has mainly focused on the development of statistical theory, methodology, and appl...
The difference-of-convex (DC) algorithm is a conceptually simple method for the minimization of (non...
Bregman divergences generalize measures such as the squared Euclidean distance and the KL divergenc...
We introduce a class of discrete divergences on sets (equivalently binary vectors) that we call the ...
The Bregman and Total Bregman divergences are useful for determining the similarity of complex data...
summary:The problem to maximize the information divergence from an exponential family is generalized...
En este trabajo se desarrollan ciertas propiedades relativas a la convexidad de la M-divergencia en ...
In image and signal processing, the beta-divergence is well known as a similarity measure between tw...
Here we examine some connections between the notions of generalized arithmetic means, geodesics, Lag...
Nonparametric convex regression has been extensively studied over the last two decades. It has been ...