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题名: Determining the Near Optimal Architecture of Autoencoder using Correlation Analysis of the Network Weights
作者: Ma, H; Lu, YG(路永钢); Zhang, HT
收录类别: CPCI-S
出版日期: 2016
会议名称: 8th International Joint Conference on Computational Intelligence
会议日期: NOV 09-11, 2016
会议地点: Porto, PORTUGAL
英文摘要: Currently, deep learning has already been successfully applied in many fields such as image recognition, recommendation systems and so on. Autoencoder, as an important deep learning model, has attracted a lot of research interests. The performance of the autoencoder can greatly be affected by its architecture. However, how to automatically determine the optimal architecture of the autoencoder is still an open question. Here we propose a novel method for determining the optimal network architecture based on the analysis of the correlation of the network weights. Experiments show that for different datasets the optimal architecture of the autoencoder may be different, and the proposed method can be used to obtain near optimal network architecture separately for different datasets.
关键词: Deep Learning ; Autoencoder ; Architecture Optimization ; Correlation Analysis
作者部门: [Ma, Heng ; Lu, Yonggang ; Zhang, Haitao] Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Gansu, Peoples R China
通讯作者: Lu, YG (reprint author), Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Gansu, Peoples R China.
学科分类: Computer Science
所属项目编号: National Natural Science Foundation of China [61272213] ; Fundamental Research Funds for the Central Universities [lzujbky-2016-k07]
所属项目名称: 国家自然科学基金项目 ; 中央高校基本科研业务费专项资金
项目资助者: NSFC ; LZU
会议录: PROCEEDINGS OF THE 8TH INTERNATIONAL JOINT CONFERENCE ON COMPUTATIONAL INTELLIGENCE, VOL 3: NCTA
页码: 53-61
出版者: SCITEPRESS
出版地: SETUBAL
语种: 英语
DOI: 10.5220/0006039000530061
WOS记录号: WOS:000393153700004
IR记录号: WOS:000393153700004
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内容类型: 会议论文
URI标识: http://ir.lzu.edu.cn/handle/262010/189709
Appears in Collections:信息科学与工程学院_会议论文

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Recommended Citation:
Ma, H,Lu, YG,Zhang, HT. Determining the Near Optimal Architecture of Autoencoder using Correlation Analysis of the Network Weights[C]. 见:8th International Joint Conference on Computational Intelligence. Porto, PORTUGAL. NOV 09-11, 2016.
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