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Squeezing Bottlenecks: Exploring the Limits of Autoencoder Semantic Representation Capabilities

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Squeezing Bottlenecks: Exploring the Limits of Autoencoder Semantic Representation Capabilities

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Gupta, PA.; Branchs, R.; Rosso, P. (2016). Squeezing Bottlenecks: Exploring the Limits of Autoencoder Semantic Representation Capabilities. Neurocomputing. 175:1001-1008. doi:10.1016/j.neucom.2015.06.091.

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Título: Squeezing Bottlenecks: Exploring the Limits of Autoencoder Semantic Representation Capabilities
Autor:
Entidad UPV: Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica
Fecha difusión:
Resumen:
We present a comprehensive study on the use of autoencoders for modelling text data, in which (differently from previous studies) we focus our attention on the various issues. We explore the suitability of two different ...[+]
Palabras clave: Text representation , Deep autoencoder
Derechos de uso: Embargado
Fuente:
Neurocomputing. (issn: 0925-2312 )
DOI: 10.1016/j.neucom.2015.06.091
Editorial:
Elsevier
Versión del editor: http://dx.doi.org/10.1016/j.neucom.2015.06.091
Código del Proyecto: info:eu-repo/grantAgreement/EC/FP7/269180/EU
Descripción: This is the author’s version of a work that was accepted for publication in Neurocomputing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Neurocomputing 175 (2016) 1001–1008. DOI 10.1016/j.neucom.2015.06.091.
Patrocinador:
WIQ-EI IRSES/ the FP 7 Marie Curie/ 269180
DIANA APPLICATIONS Finding Hidden Knowledge in Texts: Applications/ TIN2012-38603-C02-01
VLC/CAMPUS Microcluster on Multimodal Interaction in Intelligent Systems
Tipo: Artículo

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