中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 计算技术研究所
  3. 中国科学院计算技术研究所
  4. 中国科学院计算技术研究所期刊论文
  5. 英文
Weight Uncertainty in Boltzmann Machine

文献类型：期刊论文

作者Zhang, Jian1,2; Ding, Shifei1,2; Zhang, Nan1,2; Xue, Yu3
刊名Cognitive Computation
出版日期2016-12-01
卷号8期号:6页码:1064-1073
关键词RBM DBM DBN Weight uncertainty
ISSN号1866-9956
DOI10.1007/s12559-016-9429-1
英文摘要Based on restricted Boltzmann machine (RBM), the deep learning models can be roughly divided into deep belief networks (DBNs) and deep Boltzmann machine (DBM). However, the overfitting problems commonly exist in neural networks and RBM models. In order to alleviate the overfitting problem, lots of research has been done. This paper alleviated the overfitting problem in RBM and proposed the weight uncertainty semi-restricted Boltzmann machine (WSRBM) to improve the ability of image recognition and image reconstruction. First, this paper built weight uncertainty RBM model based on maximum likelihood estimation. And in the experimental section, this paper verified the effectiveness of the weight uncertainty deep belief network and the weight uncertainty deep Boltzmann machine. Second, in order to obtain better reconstructed images, this paper used the semi-restricted Boltzmann machine (SRBM) as the feature extractor and built the WSRBM. Lastly, this paper used hybrid Monte Carlo sampling and cRBM to improve the classification ability of WSDBM. The experiments showed that the weight uncertainty RBM, weight uncertainty DBN and weight uncertainty DBM were effective compared with the dropout method. And the WSDBM model performed well in image recognition and image reconstruction as well. This paper introduced the weight uncertainty method to RBM, and proposed a WSDBM model, which was effective in image recognition and image reconstruction.
资助项目National Natural Science Foundation of China[61379101] ; National Natural Science Foundation of China[61672522] ; National Key Basic Research Program of China[2013CB329502] ; Priority Academic Program Development of Jiangsu Higer Education Institutions ; Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology
WOS研究方向Computer Science ; Neurosciences & Neurology
语种英语
WOS记录号WOS:000389304300005
出版者SPRINGER
源URL[http://119.78.100.204/handle/2XEOYT63/7796]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Ding, Shifei
作者单位1.China Univ Min & Technol, Sch Comp Sci & Technol, Xuzhou 221116, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
3.Nanjing Univ Informat Sci & Technol, Coll Comp & Software, Nanjing 210044, Jiangsu, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Jian,Ding, Shifei,Zhang, Nan,et al. Weight Uncertainty in Boltzmann Machine[J]. Cognitive Computation,2016,8(6):1064-1073.
APA Zhang, Jian,Ding, Shifei,Zhang, Nan,&Xue, Yu.(2016).Weight Uncertainty in Boltzmann Machine.Cognitive Computation,8(6),1064-1073.
MLA Zhang, Jian,et al."Weight Uncertainty in Boltzmann Machine".Cognitive Computation 8.6(2016):1064-1073.

入库方式： OAI收割

来源：计算技术研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。