《Dual Graph Convolutional Networks for Graph-Based Semi-Supervised Classification》論文理解
1.DualGCN模型框架 模型採用並行的兩個簡單的前饋網絡 C o n v A , C o n v P Conv_A,Conv_P ConvA,ConvP，其區別僅僅時輸入的圖結構信息不同，並且這兩個並行圖卷積的參數是共享的。首先，上面支路利用有標籤的節點計算交叉熵損失，並對網絡參數進行訓練，得到一種後驗分佈。之後逐漸增加下面支路得到的均方差損失的權值，使得兩個損失同時對模型參數產生影響。
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相關文章
- 1. Dual Graph Convolutional Networks for Graph-Based Semi-Supervised Classification
- 2. Graph Convolutional Networks for Text Classification
- 3. 論文解讀：Graph Convolutional Networks for Text Classifification
- 4. 《SEMI-SUPERVISED CLASSIFICATION WITH GRAPH CONVOLUTIONAL NETWORKS》論文理解
- 5. 論文筆記：AAAI 2019 Graph Convolutional Networks for Text Classification
- 6. 論文閱讀總結（Graph Convolutional Networks for Text Classification）
- 7. 【論文閱讀筆記】Graph Convolutional Networks for Text Classification
- 8. Graph Convolutional Networks for Text Classification閱讀筆記
- 9. 論文《Convolutional Neural Networks for Sentence Classification》總結
- 10. 論文閱讀：《Convolutional Neural Networks for Sentence Classification》