Rational can approximate any known activation function arbitrarily well (cf. Padé Activation Units: End-to-end Learning of Flexible Activation Functions in Deep Networks): (*the dashed lines represent ...

In this paper, the superiority of rational approximation is exploited for graph signal recovering. RatioanlNet is proposed to integrate rational function and neural networks. We show that the rational ...

The rise of graph-structured data such as social networks, regulatory networks, citation graphs, and functional brain networks, in combination with resounding success of deep learning in various ...

FIGURE 1.Linear framework graph and Laplacian matrix. (A) An example graph, G, representing the binding of two ligands, each to one site, on a biomolecule, with vertices indexed 1, …, 4 as shown.The ...