skip to main content
Language:
Search Limited to: Search Limited to: Resource type Show Results with: Show Results with: Search type Index

A tutorial survey of architectures, algorithms, and applications for deep learning

APSIPA transactions on signal and information processing, 2014-01, Vol.3 (1) [Peer Reviewed Journal]

Copyright © The Authors, 2014 ;Copyright © The Authors, 2014 The online version of this article is published within an Open Access environment subject to the conditions of the Creative Commons Attribution licence http://creativecommons.org/licenses/by/3.0 ;ISSN: 2048-7703 ;EISSN: 2048-7703 ;DOI: 10.1017/atsip.2013.9

Full text available

Citations Cited by
  • Title:
    A tutorial survey of architectures, algorithms, and applications for deep learning
  • Author: Deng, Li
  • Subjects: Algorithms ; Artificial intelligence ; Artificial neural networks ; Audio data ; Belief networks ; Data processing ; Deep learning ; Distance learning ; Information processing ; Information retrieval ; Knowledge management ; Machine learning ; Natural language processing ; Neural networks ; Online tutorials ; Overview Paper ; Researchers ; Semantics ; Signal processing ; Speech ; Taxonomy ; Workshops
  • Is Part Of: APSIPA transactions on signal and information processing, 2014-01, Vol.3 (1)
  • Description: In this invited paper, my overview material on the same topic as presented in the plenary overview session of APSIPA-2011 and the tutorial material presented in the same conference [1] are expanded and updated to include more recent developments in deep learning. The previous and the updated materials cover both theory and applications, and analyze its future directions. The goal of this tutorial survey is to introduce the emerging area of deep learning or hierarchical learning to the APSIPA community. Deep learning refers to a class of machine learning techniques, developed largely since 2006, where many stages of non-linear information processing in hierarchical architectures are exploited for pattern classification and for feature learning. In the more recent literature, it is also connected to representation learning, which involves a hierarchy of features or concepts where higher-level concepts are defined from lower-level ones and where the same lower-level concepts help to define higher-level ones. In this tutorial survey, a brief history of deep learning research is discussed first. Then, a classificatory scheme is developed to analyze and summarize major work reported in the recent deep learning literature. Using this scheme, I provide a taxonomy-oriented survey on the existing deep architectures and algorithms in the literature, and categorize them into three classes: generative, discriminative, and hybrid. Three representative deep architectures – deep autoencoders, deep stacking networks with their generalization to the temporal domain (recurrent networks), and deep neural networks (pretrained with deep belief networks) – one in each of the three classes, are presented in more detail. Next, selected applications of deep learning are reviewed in broad areas of signal and information processing including audio/speech, image/vision, multimodality, language modeling, natural language processing, and information retrieval. Finally, future directions of deep learning are discussed and analyzed.
  • Publisher: Cambridge, UK: Cambridge University Press
  • Language: English
  • Identifier: ISSN: 2048-7703
    EISSN: 2048-7703
    DOI: 10.1017/atsip.2013.9
  • Source: Alma/SFX Local Collection
    DOAJ Directory of Open Access Journals
Back to results list
INSPIRE LIBRARY - TON DUC THANG UNIVERSITY (84-028) 37 755 057 Feedback
19 Nguyen Huu Tho St. Dist.7, HCM thuvien@tdtu.edu.vn

Copyright © 2017 INSPIRE LIBRARY - TON DUC THANG UNIVERSITY

Searching Remote Databases, Please Wait