An autoencoder is a type of artificialneural networkused to learn efficient data codings in an unsupervised manner. The aim of an autoencoder is to learn a representation (encoding) for a set of data, typically
在此之前,我们已经分别介绍了两种生成式模型,分别是【Deep Learning:Foundations and Concepts】生成对抗网络和【Deep Learning:Foundations and Concepts】Normalizing Flows,它们都属于非线性隐变量模型,即都是将隐变量z从隐变量空间利用非线性变换映射到数据空间,最终得到x。在这篇博客中,第三种非线性隐变量模型,也是一...
In this article, the MNIST Digit Dataset (each image: 28 X 28 pixels) is considered for the DAE case study, since it is a standard dataset used for Deep learning andcomputer vision. The applied Neural Network for this case study is the Convolutional Neural Network (CNN). Before starting w...
UFDL链接 :http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial 自编码器( Autoencoders ):(概述) 自编码器是只有一层隐藏节点,输入和输出具有相同节点数的神经网络。 自编码器的目的是求的函数 . 也就是希望是的神经网络的输出与输入误差尽量少。 由于隐藏节点数目小于输入节点, 这就表示神经网络...
Learning Deep Autoencoders without Layer-wise Trainingnull, nullArxiv
In recent years, substantial research efforts have been dedicated to addressing these drawbacks through advancements in deep learning and AE techniques. Some of the presented architectures in this area include regularization AEs, robust AE, generative AE, convolutional AE, recurrent AE, semi-supervised ...
Autoencoders are a deep learning model for transforming data from a high-dimensional space to a lower-dimensional space. They work by encoding the data, whatever its size, to a 1-D vector. This vector can then be decoded to reconstruct the original data (in this case, an image). The ...
In 2013, Diederik P. Kingma and Max Welling published a paper that laid the foundations for a type of neural network known as avariational autoencoder(VAE).1This is now one of the most fundamental and well-known deep learning architectures for generative modeling and an excellent place to sta...
We investigate deep learning autoencoders for the unsupervised recognition of phase transitions in physical systems formulated on a lattice. We focus our investigation on the 2-dimensional ferromagnetic Ising model and then test the application of the autoencoder on the anti-ferromagnetic Ising model....
Methods: Getting data ready for deep learning using Deep-N-Omics Our python package is based in Tensorflow. In order to do integrative analysis of CITE-seq data (di-omics), the following can be done, for example GSE128639. First set the data directory that containts, rna_scaled.csv.gz,...