To do so, we bring classical optimization arguments for saddle-point algorithms to the geometry of Wasserstein space.

Despite Graph Neural Networks (GNNs) have achieved remarkable accuracy, whether the results are trustworthy is still unexplored.

Figure 1: Flow-chart demonstrating the compuation for clustered attention. For more details refer to 1.1 or 3.2 in the main paper. Work done at Idiap 34th Conference on Neural Information Processing Systems (NeurIPS 2020), V ancouver, Canada. We then present the flow chart demonstrating the same. This is followed by taking the weighted average of the 3 correponding values.

Theorem 1. Suppose that ˆ X In DB models, the commonly used p is either 1 or 2. When p = 2, DURA takes the form as the one in Equation (8) in the main text. If p = 1, we cannot expand the squared score function of the associated DB models as in Equation (4). Therefore, we choose p = 2 . 2 Table 2: Hyperparameters found by grid search. Suppose that k is the number of triplets known to be true in the knowledge graph, n is the embedding dimension of entities. That is to say, the computational complexity of weighted DURA is the same as the weighted squared Frobenius norm regularizer.

However, their performance usually suffers from the overfitting problem seriously.