论文笔记：Spectral Networks and Deep Locally Connected Networks on Graphs
这篇论文发表在2014 ICLR会议，是第一篇研究将CNN泛化到非欧式空间的论文，参考资料1直接称之为“第一代GCN”。 主要贡献： 将CNN泛化到非欧几里得空间，并提出两种并列的model，分别是基于spatial和基于spectral。 1. Spatial Construction: 进行K层聚类，每一层中又会有若干个“filter”。每层与每层之间的神经元数目是通过聚类而成，将上一
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- 5. [论文笔记] [2016] [NIPS] Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
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相关文章
- 1. Spectral Networks and Deep Locally Connected Networks on Graphs
- 2. 论文笔记：Spectral Networks and Deep Locally Connected Networks on Graphs
- 3. [论文笔记] [2014] Spectral Networks and Deep Locally Connected Networks on Graphs
- 4. paper reading：[第一代GCN] Spectral Networks and Deep Locally Connected Networks on Graphs
- 5. [论文笔记] [2016] [NIPS] Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
- 6. 论文笔记：Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
- 7. Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
- 8. Deep Learning on Graphs: A Survey论文笔记
- 9. 【论文笔记】Deep Learning on Graphs: A Survey
- 10. 【论文笔记】Training Very Deep Networks - Highway Networks