VAE 作为目前(2017)最流行的生成模型之一,可用于生成训练样本中没有的样本,让人看到了 Deep Learning 强大的无监督学习能力。 如下图这张广为人知的“手写数字生成图”,就是由 VAE 产生的。 判别模型 与 生成模型 我们都知道一般有监督学习可以分为两种模型:判别模型(DM,Discriminative Model)和生成模型(GM,...
5.《Deep Learning of Part-based Representation of Data Using Sparse Autoencoders with Nonnegativity》 6.《Contractive auto-encoders: Explicit invariance during feature extraction》 7. 变分自编码器VAE:原来是这么一回事——苏剑林 声明: 所有文章都为本人的学习笔记,非商用, 目的只求在工作学习过程中通过记...
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...
【6】Greedy Layer-Wise Training of Deep Networks中,Bengio例证了 RBM可以用autoencoder来替换,能得到相当的performance;探索了DBN的训练、对连续数值输入的适用问题、Dealing with uncooperative input distributions等。 【7】Extracting and Composing Robust Features withDenoising Autoencoders 处理带噪声/遮挡的图像...
Deep learning, which is a subfield of machine learning, has opened a new era for the development of neural networks. The auto-encoder is a key component of deep structure, which can be used to realize transfer learning and plays an important role in both unsupervised learning and non-linear...
For each nodeiin layerl, set Compute the desired partial derivatives, which are given as: 对于矩阵,在MATLAB中如下 Perform a feedforward pass, computing the activations for layers , , up to the output layer , using the equations defining the forward propagation steps ...
Improving Automatic Source Code Summarization via Deep Reinforcement Learning 1 本文背景 软件维护占据软件开发生命周期很大一部分,提供代码执行任务的描述对于软件维护来说是必须的,然而注释代码仍然是一项劳动密集型的任务,使得真实的软件项目很少具备充分的代码文档以减少未来的维护成本。 本文作者提出:一个好的注释论...
Machine learning (ML) and consequently data science as a whole have seen rapid development over the last decade or so, due largely to considerable advances in implementations and hardware that have made computations more accessible. Conceptually, the ML approach can be regarded as a data modeling ...
One of the learning aspects under consideration was the problem of accumulating the database for learning. Statistical skill learning needs a database to generalize from and training of a deep autoencoder requires an even larger database [16]. In this paper we show that an autoencoder trained ...
Methods: Deep Learning with Deep-N-Omics Mono-omic Data If you only have or choose to use mono-omic data (such as RNA expression from scRNA-seq), you can use the function gene_only_encoder(). 'GSE128639' and 'gene_only' are supplied here to save the models in the directory 'saved...