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Restricted boltzmann machine with adaptive local hidden units
文献类型:会议
作者:Cao, Binbin[1]  Li, Jianmin[2]  Wu, Jun[3]  Zhang, Bo[4]  
机构:[1]Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
[2]Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
[3]School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710072, China
[4]Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
年:2013
会议名称:20th International Conference on Neural Information Processing, ICONIP 2013
页码范围:307-314
会议地点:Daegu, Korea, Republic of
会议开始日期:2013-11-03
会议结束日期:2013-11-07
收录情况:EI(20140617280518)  
所属部门:电子信息学院
人气指数:556
浏览次数:537
语言:外文
摘要:
Deep belief network (DBN) shows the ability to learn hierarchical feature representation from image datasets which mimics the hierarchical organization of the mammal visual cortex. DBN is composed of a stack of Restricted Boltzmann Machines (RBM) which serves as feature extractors. A number of variants of RBM have been proposed to learn feature representations similar to gabor filters. They require extracting small image patches first. As images vary among different datasets, it is preferable to ...More
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