Modern deep neural networks, while strong in many domains [29], have notmastered comparable language-basedgeneralization challenges, afactconjectured tounderlie their sample inefficiencyand inflexibility [26,25,8].

Ifnosuch(mu,ml) exist, thenMAG( , , ,H,U, )= ;.

Let θ and φ denote the optimal parameters after model training. The detailed statistics of three datasets used in this paper are listed in Table 1. In this paper, when evaluating the performance in the standard experimental scenario and in the label-scarce scenario, we compare with six state-of-the-art baselines used for graph-based semisupervised learning. Three of them are deterministic GNN-based models, which are GCN [1], Graph Attention Networks(GAT)[2]andGraphSAGE[3]respectively.

Graph Neural Networks (GNNs) have achieved remarkable performance in the task of the semi-supervised node classification. However,most existing models learn a deterministic classification function, which lack sufficient flexibility to explore better choices in the presence of kinds of imperfect observed data such as the scarce labeled nodes and noisy graph structure.

In casemin(n1,n2), W also follows suit, given thatLremains constant. The exact value ofE2 = 328 K(0) can be obtained based on the convention that K(.) achieves its modalvalueat0. To unify the two processes, one may assess theconvergencebasedonn=min{n1,n2}. Moreover, µ ν 1 F#µ ν 1 µ F#µ 1. (15) If the forward translated lawF#µ is Sobolev-smooth of ordermy (Assumption 2), Theorem (3) asserts the existence of a constantR As such, the cumulative identity loss from both domains cannot be minimized beyond the intrinsic discrepancy between the input distributions.