Probabilistic End-to-End Graph-based ...
Type de document :
Autre communication scientifique (congrès sans actes - poster - séminaire...): Communication dans un congrès avec actes
Titre :
Probabilistic End-to-End Graph-based Semi-Supervised Learning
Auteur(s) :
Vargas Vieyra, Mariana [Auteur]
Machine Learning in Information Networks [MAGNET]
Bellet, Aurelien [Auteur]
Machine Learning in Information Networks [MAGNET]
Denis, Pascal [Auteur]
Machine Learning in Information Networks [MAGNET]
Machine Learning in Information Networks [MAGNET]
Bellet, Aurelien [Auteur]
Machine Learning in Information Networks [MAGNET]
Denis, Pascal [Auteur]
Machine Learning in Information Networks [MAGNET]
Titre de la manifestation scientifique :
Graph Representation Learning workshop, NeurIPS
Ville :
Vancouver
Pays :
Canada
Date de début de la manifestation scientifique :
2019
Date de publication :
2019
Discipline(s) HAL :
Informatique [cs]/Intelligence artificielle [cs.AI]
Résumé en anglais : [en]
In this paper we address the problem of graph-based semi-supervised learning in tasks where a graph describing the relationships between data points is not available. We propose a method to jointly learn the graph and the ...
Lire la suite >In this paper we address the problem of graph-based semi-supervised learning in tasks where a graph describing the relationships between data points is not available. We propose a method to jointly learn the graph and the parameters of a semi-supervised model using a probabilistic framework. We empirically show our proposal achieves competitive results in a variety of datasets.Lire moins >
Lire la suite >In this paper we address the problem of graph-based semi-supervised learning in tasks where a graph describing the relationships between data points is not available. We propose a method to jointly learn the graph and the parameters of a semi-supervised model using a probabilistic framework. We empirically show our proposal achieves competitive results in a variety of datasets.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :
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- distributional_end2end_semi-supervised_learning.pdf
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