論文筆記：Spectral Networks and Deep Locally Connected Networks on Graphs
這篇論文發表在2014 ICLR會議，是第一篇研究將CNN泛化到非歐式空間的論文，參考資料1直接稱之爲「第一代GCN」。 主要貢獻： 將CNN泛化到非歐幾里得空間，並提出兩種並列的model，分別是基於spatial和基於spectral。 1. Spatial Construction: 進行K層聚類，每一層中又會有若干個「filter」。每層與每層之間的神經元數目是通過聚類而成，將上一
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- 1. Spectral Networks and Deep Locally Connected Networks on Graphs
- 2. 論文筆記：Spectral Networks and Deep Locally Connected Networks on Graphs
- 3. [論文筆記] [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. [論文筆記] [2016] [NIPS] Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
- 6. 論文筆記：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論文筆記
- 9. 【論文筆記】Deep Learning on Graphs: A Survey
- 10. 【論文筆記】Training Very Deep Networks - Highway Networks
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相關文章
- 1. Spectral Networks and Deep Locally Connected Networks on Graphs
- 2. 論文筆記：Spectral Networks and Deep Locally Connected Networks on Graphs
- 3. [論文筆記] [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. [論文筆記] [2016] [NIPS] Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
- 6. 論文筆記：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論文筆記
- 9. 【論文筆記】Deep Learning on Graphs: A Survey
- 10. 【論文筆記】Training Very Deep Networks - Highway Networks