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-Encoders—— 半隐式图变分自动编码器 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...
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/... ...
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)...
Constrained Graph Variational Autoencoders for Molecule Design Qi Liu, Miltos Allamanis, Marc Brockschmidt, Alexander Gaunt NIPS 2018|December 2018 Download BibTex Graphs are ubiquitous data structures for representing interactions between entities. With an emphasis on the use of graphs to represent...
Kipf, T.N., Welling, M.: Variational graph auto-encoders. NIPS Workshop on Bayesian Deep Learning. (2016) Chien, E., Peng, J., Li, P., Milenkovic, O.: Adaptive universal generalized pagerank graph neural network. In: International Conference on Learning Representations (2020) Sen, P....
Liu Q, Allamanis M, Brockschmidt M, Gaunt A (2018) Constrained graph variational autoencoders for molecule design. NeurIPS 2018:7806–7815 MATH Google Scholar Simonovsky M, Komodakis N (2018) GraphVAE: towards generation of small graphs using variational autoencoders. ICANN 2018, Part I. ...
https://towardsdatascience.com/variational-inference-derivation-of-the-variational-autoencoder-vae-loss-function-a-true-story-3543a3dc67ee [10] 高校数学の美しい物語:対数和不等式の証明と応用https://mathtrain.jp/logsumineq Cite As Kenta (2025).Conditional VAE (Variational ...
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/......
Variational autoencoders (VAEs) play an important role in high-dimensional data generation based on their ability to fuse the stochastic data representation with the power of recent deep learning techniques. The main advantages of these types of generators lie in their ability to encode the informa...