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Compressed auto-encoder building block for deep learning network
Feng Q.; Chen C.L.P.; Chen L.
2016-10-07
Conference Name3rd International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS)
Source Publication2016 3rd International Conference on Informative and Cybernetics for Computational Social Systems, ICCSS 2016
Pages131-136
Conference DateAUG 26-29, 2016
Conference PlaceJinzhou, PEOPLES R CHINA
Abstract

Deep learning algorithm has been widely used in many area which is one of the most important representation learning algorithms in machine learning tasks. Deep learning network is stacked by the building blocks such as the restricted Boltzmann machine(RBM) and the auto-encoder, convolutional building block. After stacking the building blocks layers and layers, the improvement of the deep learning network would be notable. In this paper, we proposed a new deep learning building block that inspired by the auto-encoder, which is the compressed auto-encoder with fewer layers and parameters compared with the auto-encoder, and we put forward a bidirectional gradient decent method to update the parameters of this building block. As the experimental results show that improves the performance of the auto-encoder in accuracy of the reconstruction data. It keeps declining the error while the results of rbm or the auto-encoder becomes saturation, and some analysis are given in this paper.

KeywordBack Propagation Bidirectional Gradient Decent Compressed Auto-encoder Rbm
DOI10.1109/ICCSS.2016.7586437
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Cybernetics ; Engineering, Electrical & Electronic
WOS IDWOS:000390239500026
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Citation statistics
Cited Times [WOS]:1   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
AffiliationUniversidade de Macau
Recommended Citation
GB/T 7714
Feng Q.,Chen C.L.P.,Chen L.. Compressed auto-encoder building block for deep learning network[C],2016:131-136.
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