Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/14221
Title: A survey of deep neural network architectures and their applications
Authors: Liu, W
Wang, Z
Liu, X
Zeng, N
Liu, Y
Alsaadi, FE
Keywords: Autoencoder;Convolutional neural network;Deep learning;Deep belief network;Restricted Boltzmann machine
Issue Date: 2017
Publisher: Elsevier
Citation: Neurocomputing, 234: pp. 11 - 26, (2017)
Abstract: Since the proposal of a fast learning algorithm for deep belief networks in 2006, the deep learning techniques have drawn ever-increasing research interests because of their inherent capability of overcoming the drawback of traditional algorithms dependent on hand-designed features. Deep learning approaches have also been found to be suitable for big data analysis with successful applications to computer vision, pattern recognition, speech recognition, natural language processing, and recommendation systems. In this paper, we discuss some widely-used deep learning architectures and their practical applications. An up-to-date overview is provided on four deep learning architectures, namely, autoencoder, convolutional neural network, deep belief network, and restricted Boltzmann machine. Different types of deep neural networks are surveyed and recent progresses are summarized. Applications of deep learning techniques on some selected areas (speech recognition, pattern recognition and computer vision) are highlighted. A list of future research topics are finally given with clear justifications.
URI: http://bura.brunel.ac.uk/handle/2438/14221
DOI: http://dx.doi.org/10.1016/j.neucom.2016.12.038
ISSN: 0925-2312
Appears in Collections:Dept of Computer Science Research Papers

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