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UC伯克利聯合清華提出隱式圖神經網絡

2020-09-16

33

9月16日消息，UC伯克利和清華的研究人員提出了一個圖學習框架，稱為隱式圖神經網絡（IGNN），其中的預測是基于一個涉及隱式定義的“狀態” 向量的不動點平衡方程的解。利用Perron-Frobenius理論推導了保證框架良好性的充分條件。利用隱式微分，研究人員推出了一個易于處理的投影梯度下降方法來訓練框架。對一系列任務進行的實驗表明，IGNN始終能夠捕獲長期依賴關系，并優于最先進的GNN模型。

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