A survey of deep neural network architectures and their applications

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 ap...

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Vydáno v:	Neurocomputing (Amsterdam) Ročník 234; s. 11 - 26
Hlavní autoři:	Liu, Weibo, Wang, Zidong, Liu, Xiaohui, Zeng, Nianyin, Liu, Yurong, Alsaadi, Fuad E.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier B.V 19.04.2017
Témata:	Autoencoder Convolutional neural network Deep belief network Deep learning Restricted Boltzmann machine Deep learning Restricted Boltzmann machine Deep belief network Autoencoder Convolutional neural network
ISSN:	0925-2312, 1872-8286
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

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.
AbstractList	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.
Author	Zeng, Nianyin Alsaadi, Fuad E. Liu, Weibo Liu, Yurong Wang, Zidong Liu, Xiaohui
Author_xml	– sequence: 1 givenname: Weibo surname: Liu fullname: Liu, Weibo organization: Department of Computer Science, Brunel University London, Uxbridge, Middlesex, UB8 3PH, United Kingdom – sequence: 2 givenname: Zidong orcidid: 0000-0002-9576-7401 surname: Wang fullname: Wang, Zidong email: Zidong.Wang@brunel.ac.uk organization: Department of Computer Science, Brunel University London, Uxbridge, Middlesex, UB8 3PH, United Kingdom – sequence: 3 givenname: Xiaohui surname: Liu fullname: Liu, Xiaohui organization: Department of Computer Science, Brunel University London, Uxbridge, Middlesex, UB8 3PH, United Kingdom – sequence: 4 givenname: Nianyin surname: Zeng fullname: Zeng, Nianyin organization: Department of Instrumental and Electrical Engineering, Xiamen University, Xiamen 361005, Fujian, China – sequence: 5 givenname: Yurong surname: Liu fullname: Liu, Yurong organization: Department of Mathematics, Yangzhou University, Yangzhou 225002, China – sequence: 6 givenname: Fuad E. surname: Alsaadi fullname: Alsaadi, Fuad E. organization: Communication Systems and Networks (CSN) Research Group, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
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PublicationYear	2017
Publisher	Elsevier B.V
Publisher_xml	– name: Elsevier B.V
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Snippet	Since the proposal of a fast learning algorithm for deep belief networks in 2006, the deep learning techniques have drawn ever-increasing research interests...
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