Spectral Networks and Deep Locally Connected Networks on Graphs
Paper : Spectral Networks and Deep Locally Connected Networks on Graphs Code : 摘要 Yann LeCun 組在nips2014發表的有關卷積在圖上遷移的理論研究成果，可能是最早的一篇研究Graph Convolution 的文章。在歐式空間中使用的卷積具有平移不變性，權值共享，局部連接，分層次表達的特點，但是圖結構是一
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相關文章
- 1. Spectral Networks and Deep Locally Connected Networks on Graphs
- 2. paper reading：[第一代GCN] Spectral Networks and Deep Locally Connected Networks on Graphs
- 3. 論文筆記：Spectral Networks and Deep Locally Connected Networks on Graphs
- 4. [論文筆記] [2014] Spectral Networks and Deep Locally Connected Networks on Graphs
- 5. Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
- 6. Understanding Locally Connected Layers In Convolutional Neural Networks
- 7. SpectralNet : spectral clustering using deep neural networks
- 8. [論文筆記] [2016] [NIPS] Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
- 9. 論文筆記：Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
- 10. Neural Networks and Deep Learning -- Class 4: Deep Neural Networks