图生成模型:变分图自编码器(VGAE) 变分自编码器的训练过程 VAE的本质 VAE虽然也称是AE(AutoEncoder)的一种,但它的做法(或者说它对网络的诠释)是别具一格的。在VAE中,它的Encoder有两个,一个用来计算均值,一个用来计算方差,这已经让人意外了:Encoder不是用来Encode的,是用来算均值和方差的,这真是大新闻了,还...
虽然最近提出了一种模型GraphMAE来弥合差距,但其在无监督学习任务中的性能仍然未知。在本文中,为了全面提高生成图SSL在无监督和监督学习任务中相对于其他GCL模型的性能,我们提出了SeeGera模型,该模型基于自监督变分图自动编码器(VGAE)家族。具体来说,SeeGera采用了半隐式变分推理框架,一种层次变分框架,主要关注特征...
variational graph auto-encoder adversarial learning mutual information maximization 摘要 With the success of Graph Neural Network (GNN) in network data, some GNN-based representation learning methods for networks have emerged recently. Variational Graph Autoencoder (VGAE) is a basic GNN framework for ...
In this study, we present a deep learning framework with variational graph auto-encoder for miRNA-disease association prediction (VGAE-MDA). VGAE-MDA first gets the representations of miRNAs and diseases from the heterogeneous networks constructed by miRNA-miRNA similarity, disease-disease similarity,...
With the success of Graph Neural Network (GNN) in network data, some GNN-based representation learning methods for networks have emerged recently. Variational Graph Autoencoder (VGAE) is a basic GNN framework for network representation. Its purpose is to well preserve the topology and node attribu...
VARIATIONAL RECURRENT AUTO-ENCODERS 详解 摘要 在本文中,我们提出了一个结合了RNN和SGVB优势的模型:变分自动编码器(VRAE)。 这种模型可用于对时间序列数据进行有效的大规模无监督学习,将时间序列数据映射到潜在向量表示。 该模型是生成模型,因此可以从隐藏空间的样本生成数据。 这项工作的一个重要贡献是该模型可以...
In this study, based on signed message propagation, we propose a framework, Multi-scale Sign Variational Graph AutoEncoder (MSignVGAE), for microbe-disease signed association prediction. MSignVGAE utilizes a graph variational autoencoder to model noisy signed association data and extends the multi...
while graph autoencoders propagate labels via known lncRNA-disease associations. These two kinds of autoencoders are trained alternately by adopting variational expectation maximization algorithm. The integration of both the VGAE for graph representation learning, and the alternate training via variational...
由于变分推断主要运用于贝叶斯学习的场景下,我们首先简单介绍贝叶斯学习,引入变分推断方法,并且最后给出一个采用变分推理方法求解传统共轭模型的简单例子(这部分会在变分推断方法简介02中推出):变分方法求解一元高斯。以后我们会介绍非共轭模型的求解并给出一个例子: 变分自编码器VAE(variational autoencoder)的求解。
Variational Graph Normalized AutoEncoder 本文提出了两种变体的图自编码器,分别称为图归一化自编码器(GNAE)和变分图归一化自编码器(VGNAE)。对于每个节点,GNAE对其邻域的局部结构信息和节点特征信息进行编码,从而推导出潜在变量 我们还提出了一个变分图归一化自动编码器(VGNAE)。由于VGAEs中的平均向量也具有孤立节...