Reducing the Dimensionality of Data with Neural Networks.pdf,Reducing the Dimensionality of Data with Neural Networks G. E. Hinton, et al. Science 313, 504 (2006); DOI: 10.1126/science.1127647 The following resources related to this article are available
Reducing the dimensionality of data with neural networks.pdfof,帮助,the,Data,with,data,The,With 文档格式: .pdf 文档大小: 416.37K 文档页数: 5页 顶/踩数: 0/0 收藏人数: 0 评论次数: 0 文档热度: 文档分类: 经济/贸易/财会--财政/国家财政 ...
Reducing the dimensionality of data with neural networks pdf Why dimensionality reduction is important in machine learning. Why reduce dimensionality of data. Reducing dimensionality of data with neural networks. What is dimensionality of data. High-dimensional data can be converted to low-dimensional ...
深度学习论文Hinton, Geoffrey E., and Ruslan R. Salakhutdinov. Reducing the dimensionality of data with neural networks..pdf,REPORTS larized nearly vertically. For completeness, Fig. metamaterials would be highly desirable but is 1B shows the off-resonant
the initial weights are close to a good solution. We describe an effective way of initializing the weights that allows deep autoencoder networks to learn low-dimensional codes that work much better than principal components analysis as a tool to reduce the dimensionality of data. ...
深度学习经典论文分析(四)-Reducing the dimensionality of data with neural networks Be Tough 清华大学 控制科学与工程博士在读57 人赞同了该文章 目录 收起 本篇文章目录如下: 1 文章想要解决的问题 1.1 BP算法的梯度消失 1.2 神经网络构成的非线性降维算法 2 研究的是否是一个新问题 2.1 梯度消失的...
内容提示: www.sciencemag.org/cgi/content/full/313/5786/504/DC1 Supporting Online Material for Reducing the Dimensionality of Data with Neural Networks G. E. Hinton* and R. R. Salakhutdinov *To whom correspondence should be addressed. E-mail: hinton@cs.toronto.edu Published 28 July 2006, ...
权重系数的变化可以表示为:\[\Delta w_{ij} = \epsilon(<v_i h_j>_{data} - <v_i h_j>_{recon})\],其中\(\epsilon\)是学习率,\(<v_i h_j>_{data}\) 表示数据中像素i和特征检测器j同为1的频率,\(<v_i h_j>_{recon}\) 是对应生成图片中为1的频率。相同学习规则的简化版本用于偏差...
weights that allows deep autoencoder networks to learn low-dimensional codes that work much better than principal components analysis as a tool to reduce the dimensionality of data. (0)踩踩(0) 所需:1积分
论文“Reducing the Dimensionality of Data with Neural Networks”是深度学习鼻祖hinton于2006年发表于《SCIENCE 》的论文,也是这篇论文揭开了深度学习的序幕。 笔记 摘要:高维数据可以通过一个多层神经网络把它编码成一个低维数据,从而重建这个高维数据,其中这个神经网络的中间层神经元数是较少的,可把这个神经网络叫做...