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dc.contributor.authorMasegosa Arredondo, Andrés Ramón
dc.contributor.authorCabañas de Paz, Rafael
dc.contributor.authorLangseth, Helge
dc.contributor.authorNielsen, Thomas Dyhre
dc.contributor.authorSalmerón Cerdán, Antonio
dc.date.accessioned2021-02-01T09:04:54Z
dc.date.available2021-02-01T09:04:54Z
dc.date.issued2021-01-18
dc.identifier.issn1099-4300
dc.identifier.urihttp://hdl.handle.net/10835/9516
dc.description.abstractRecent advances in statistical inference have significantly expanded the toolbox of probabilistic modeling. Historically, probabilistic modeling has been constrained to very restricted model classes, where exact or approximate probabilistic inference is feasible. However, developments in variational inference, a general form of approximate probabilistic inference that originated in statistical physics, have enabled probabilistic modeling to overcome these limitations: (i) Approximate probabilistic inference is now possible over a broad class of probabilistic models containing a large number of parameters, and (ii) scalable inference methods based on stochastic gradient descent and distributed computing engines allow probabilistic modeling to be applied to massive data sets. One important practical consequence of these advances is the possibility to include deep neural networks within probabilistic models, thereby capturing complex non-linear stochastic relationships between the random variables. These advances, in conjunction with the release of novel probabilistic modeling toolboxes, have greatly expanded the scope of applications of probabilistic models, and allowed the models to take advantage of the recent strides made by the deep learning community. In this paper, we provide an overview of the main concepts, methods, and tools needed to use deep neural networks within a probabilistic modeling framework.es_ES
dc.language.isoenes_ES
dc.publisherMDPIes_ES
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectdeep probabilistic modelinges_ES
dc.subjectvariational inferencees_ES
dc.subjectneural networkses_ES
dc.subjectlatent variable modelses_ES
dc.subjectBayesian learninges_ES
dc.titleProbabilistic Models with Deep Neural Networkses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherversionhttps://www.mdpi.com/1099-4300/23/1/117es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES


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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internacional