我们的目标是通过一个具有固定构型(层数、激活函数等,这样权值就是f的足够统计量)的神经网络来学习一个函数y=f(x),这为x和y之间的关系提供了一个可能的解释。权值W用贝叶斯方法建模为随机变量,并在它们上引入先验分布。由于W不是确定性的,因此神经网络的输出也是一个随机变量。通过对W的后验分布进行积分,可以...
下载地址:Bayesian Graph Convolutional Neural Networks for Semi-Supervised Classification 摘要: 最近,将卷积神经网络应用于图结构数据的技术已经出现。图卷积神经网络(gcnn)已被用于解决节点和图分类以及矩阵补全。尽管其性能令人印象深刻,但目前的实现在将不确定性纳入图结构方面的能力有限。几乎所有的gcnn处理图,就好...
Bayesian Graph Convolutional Neural Networks for Semi-Supervised Classificationdoi:10.1609/AAAI.V33I01.33015829Yingxue ZhangSoumyasundar PalMark CoatesDeniz UstebayAssociation for the Advancement of Artificial Intelligence (AAAI)National Conference on Artificial Intelligence...
Gal, Y., Ghahramani, Z.: Bayesian convolutional neural networks with Bernoulli approximate variational inference. arXiv preprint arXiv:1506.02158 (2015) 2Yarin Gal and Zoubin Ghahramani. Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference. arXiv: 1506.02158, 2015. doi:...
In recent years, it has been shown that, compared to other contemporary machine learning models, graph convolutional networks (GCNs) achieve superior perfo
We propose a Bayesian convolutional neural network built upon Bayes by Backprop and elaborate how this known method can serve as the fundamental construct of our novel reliable variational inference method for convolutional neural networks. First, we show how Bayes by Backprop can be applied to convo...
This is a TensorFlow implementation of "Bayesian Graph Convolutional Neural Networks" for the task of (semi-supervised) classification of nodes in a graph, as described in our paper:Yingxue Zhang*, Soumyasundar Pal*, Mark Coates, Deniz Üstebay, Bayesian graph convolutional neural networks for ...
In this section, the basic theory of EEG source localization and dynamical graph convolutional neural networks will be presented. EEG source localization EEG source localization method provides spatio-temporal information about the activity of different areas of the brain. Brain source localization improves...
Bayesian Hierarchical convolutional neural networks 来自 Semantic Scholar 喜欢 0 阅读量: 2 作者:A Bensen,A Kahana,Z Woods 摘要: The Hierarchical Bayesian Neural Network (HNN) is a machine learning algorithm that attempts to use the natural hierarchical structure of data. HNN has demonstrated gains ...
Batzner, S. et al. E (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials. , 3564–3572 (2019). Jinnouchi, R., Lahnsteiner, J., Karsai, F., Kresse, G. & Bokdam, M. Phase transitions of hybrid perovskites simulated by machine-learning force fields...