Reducing the Dimensionality of Data with Neural Networks.pdf 5页VIP内容提供方:554389950 大小:416.37 KB 字数:约6.63万字 发布时间:2016-03-09发布于广东 浏览人气:695 下载次数:仅上传者可见 收藏次数:0 需要金币:*** 金币 (10金币=人民币1元)Reducing the
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Fig. 1.Pretraining consists of learning a stack of restricted Boltzmann machines (RBMs), eachhaving only one layer of feature detectors. Thelearned feature activations of one RBM are usedas the ‘‘data’’ for training the next RBM in the stack. After the pretraining, the RBMs are‘‘unr...
然后再次更新隐含单元的状态,这样才能表示虚构出来的图片。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...
同时,还实验了在没有预训练的情况下,即使用BP训练非常久,DAE也总是重构数据的平均值(没看懂这句,原话为always reconstructs the average of the training data) 只有单隐藏层的Autoencoder虽然可以在没有预训练的情况下学习,但是加入预训练后能大大减少训练总耗时 用相同的参数量分别搭建深层、浅层的自编码器,深...
06论文笔记《Reducing the Dimensionality of Data with Neural Networks》,程序员大本营,技术文章内容聚合第一站。
Connolly, "Reducing the dimensionality of data: Locally linear embedding of sloan galaxy spectra", The Astronomical Journal, vol. 138, no. 5, pp. 1365, 2009.Vanderplas, J., & Connolly, A. (2009). Reducing the dimensionality of data: Locally linear embedding of Sloan Galaxy Spectra. The ...
[论文翻译]Reducing the Dimensionality of Data with Neural Networks,程序员大本营,技术文章内容聚合第一站。
[笔记]Reducing the Dimensionality of Data with Neural Networks 原文链接:Reducing the Dimensionality of Data with Neural Networks 主要思想 (下方大量公式,貌似只能在pc上看。移动端无法显示。) 利用多层神经网络构建encoder,通过优化重构后(reconstruction)的输入$x_{recon}$和原输入$x$之间的cross-entropy函数,...
通过训练多层神经网络可以将高维数据转换成低维数据,其中有对高维输入向量进行改造的网络层。梯度下降可以用来微调如自编码器网络的权重系数,但是对权重的初始化要求比较高。这里提出一种有效初始化权重的方法,允许自编码器学习低维数据,这种降维方式比PCA表现效果更好