论文地址:Kipf T N, Welling M. Variational graph auto-encoders[J]. NIPS, 2016. 代码地址: https://github.com/tkipf/gae图神经网络可以细分为五类:图卷积网络、图注意力网络、图时空网络、图生成网络和图自…
Semi-implicit graph variational auto-encoder (SIG-VAE) 实验部分 来自NIPS 2019 德州农机大学 本文关注的问题 本文提出了半隐式图变分自动编码器(SIG-VAE),扩展VGAE对图数据建模的灵活性。SIG-VAE采用分层变分框架,使相邻节点共享能够更好的图依赖结构的生成建模。 VAE的局限 当给定图的真实后验分布明显违反高...
论文:Variational Graph Auto-Encoders阅读笔记 作者:Thomas N. Kipf, Max Welling, 和GCN的作者是一样的 会议:Bayesian Deep Learning Workshop (NIPS 2016), NIPS的一个workshop,不是长文 论文链接:Variational Graph Auto-Encoders 代码链接:tkipf/... 查看原文 图神经网络五大类之一 VGAE(变分图自编码器)...
论文:Variational Graph Auto-Encoders阅读笔记 论文:Variational Graph Auto-Encoders阅读笔记 作者:Thomas N. Kipf, Max Welling, 和GCN的作者是一样的 会议:Bayesian Deep Learning Workshop (NIPS 2016), NIPS的一个workshop,不是长文 论文链接:Variational Graph Auto-Encoders 代码链接:tkipf/......
Finally, we show that 𝒮𝒮\mathcal{S}-VAEs can significantly improve link prediction performance on citation network datasets in combination with a Variational Graph Auto-Encoder (VGAE) (Kipf and Welling,, 2016). (a) Original (b) Autoencoder (c) 𝒩𝒩\mathcal{N}-VAE (d) ...
Paper tables with annotated results for Micro and Macro Level Graph Modeling for Graph Variational Auto-Encoders
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 Inference in Recommendation: Variational inference (VI) has been applied in recommendation but coupling with AutoEncoders, i.e., Variaitonal AutoEncoders (VAEs) (Kingma and Welling, 2013). In recommendation, VAEs concentrate on Collaborative filtering and are trying to model the uncert...
Variational Autoencoder (VAE) Proposed based on variational bayes and graph model VAE with Autoregressive Flow Prior Generate Priori Distribution by Autoregressive Flow and Inverse Autoregressive Flow Auxiliary Autoencoder Beta-VAE VQ-VAE The core is vector quantization VQ-VAE2 introduce the multi-sca...
Challis, E. and Barber, D. Affifine independent variational inference. InNIPS, 2012. 随机反向传播的两个步骤: 1.重参数化:我们根据一个已知的分布和一个可微分的变换来重参数化隐变量(比如一位位置尺度变换或累计分布函数)比如,如果q(z)是一个高斯分布 ...