《 ROP EDGE : TOWARDS DEEP GRAPH CONVOLU - TIONAL NETWORKS ON NODE CLASSIFICATION》
摘要 過擬合和過度平滑是發展深度圖卷積網絡進行節點分類（Node classification）的兩個主要障礙。特別是過擬合會削弱小數據集的泛化能力，而過平滑會隨着網絡深度的增加而將輸出表示從輸入特徵中分離出來，從而阻礙模型的訓練。本文提出了一種新穎靈活的技術DropEdge來緩解這兩個問題。在其核心，DropEdge在每個訓練元隨機地從輸入圖中刪除一定數量的邊，充當數據擴充器和消息傳遞減速器。此
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相關文章
- 1. 【論文解讀 ICLR 2020 | DropEdge】TOWARDS DEEP GRAPH CONVOLU-TIONAL NETWORKS ON NODE CLASSIFICATION
- 2. 論文筆記：DROPEDGE TOWARDS DEEP GRAPH CONVOLUTIONAL NETWORKS ON NODE CLASSIFICATION
- 3. 深度圖卷積網絡系列文章（二）---DropEdge:Toward Deep Graph Convolutional Networks on Node Classification
- 4. Paper Notes: A Comprehensive Survey on Graph Neural Networks
- 5. GNN圖神經網絡 DropEdge：towards deep GCN on node classfication
- 6. Structural Image Classification with Graph Neural Networks
- 7. Edge Intelligence: On-Demand Deep Learning Model Co-Inference with Device-Edge Synergy
- 8. Towards Alzheimer’s Disease Classification through Transfer Learning
- 9. Towards Deeper Graph Neural Networks
- 10. Towards Sparse Hierarchical Graph Classifiers