Gaussian mixture variational autoencoderMixture symmetric Kullback-Leibler divergenceMixture probabilistic principal component regressionJust-in-time learningIndustrial data are often high-dimensional, nonlinear
1. 引言 这篇博文主要是对论文“Deep Clustering by Gaussian Mixture Variational Autoencoders with Graph Embedding”的整理总结,这篇文章将图嵌入与概率深度高斯混合模型相结合,使网络学习到符合全局模型和局部结构约束的强大特征表示。将样本作为图上的节点,并最小化它们的后验分布之间的加权距离,在这里使用Jenson-...
Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders 1. GMVAE的基本原理 高斯混合变分自编码器(GMVAE)是一种结合高斯混合模型(GMM)和变分自编码器(VAE)的模型,旨在解决标准VAE中隐空间表示单态(unimodal)的问题。GMVAE使用GMM作为先验分布,以表示更加复杂的先验表示,从而能够捕获数据中的多态性...
This repository contains an implementation of the Gaussian Mixture Variational Autoencoder (GMVAE) based on the paper "A Note on Deep Variational Models for Unsupervised Clustering" by James Brofos, Rui Shu, and Curtis Langlotz and a modified version of the M2 model proposed by D. P. Kingma ...
DEEP UNSUPERVISED CLUSTERING WITH GAUSSIAN MIXTURE VARIATIONAL AUTOENCODERS(ICLR2017),程序员大本营,技术文章内容聚合第一站。
Here, we present autoCell, a graph-embedded Gaussian mixture variational autoencoder network algorithm for scRNA-seq dropout imputation and feature extraction. Our autoCell provides a deep-learning toolbox for end-to-end analysis of large-scale single-cell/nucleus RNA-seq data, including ...
Deep Clustering by Gaussian Mixture Variational Autoencoders with Graph Embedding(DGG) 文章目录 写在前面 摘要 1. 介绍 2. 相关工作 3. DGG 3.1 深度高斯混合模型(Deep GMM) 3.2 图嵌入的VAE 3.2.1 学习算法 3.3 构建邻接矩阵 4. 实验 5. 结论 写在前面 这个论文讲聚类的准确率拉到一个非常高的值...
Cvetko, T.: Autoencoders for translation (2020) Google Scholar Dilokthanakul, N., et al.: Deep unsupervised clustering with Gaussian mixture variational autoencoders. arXiv preprintarXiv:1611.02648(2016) Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)...
Linxiao Yang, Ngai-Man Cheung, Jiaying Li, and Jun Fang, "Deep Clustering by Gaussian Mixture Variational Autoencoders with Graph Embedding", In ICCV 2019. - ngoc-nguyen-0/DGG
A just-in-time modeling approach for multimode soft sensor based on Gaussian mixture variational autoencoder Computers & Chemical Engineering, Volume 146, 2021, Article 107230 Fan Guo,…, Biao Huang A new unsupervised data mining method based on the stacked autoencoder for chemical process fault ...