论文笔记之Learning Convolutional Neural Networks for Graphs
本篇论文是2016ICML上的一篇论文，对于如何将cnn应用在graph上提供了一种新的思路。web 架构： 整体上讲，就是用w个固定size=（k+1）的子图来表示输入的graph，再将这w个子图正则化后,生成一个w(k+1)维的向量，做为传统的cnn的输入，进行学习。其实就是作了一个从graph到向量的映射的一个预处理过程。算法 算法流程 输入：任意一张图 输出：每一个channel输出w个r
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相关文章
- 1. Learning Convolutional Neural Networks for Graphs论文笔记
- 2. Learning Convolutional Neural Networks for Graphs
- 3. 【Fast Algorithms for Convolutional Neural Networks】论文笔记
- 4. 【论文笔记】Weakly Supervised Learning Based on Coupled Convolutional Neural Networks for Aircraft Detecti
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- 9. Convolutional Networks on Graphs for Learning Molecular Fingerprints
- 10. 论文笔记：Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering