Abstract: Equivariant quantum graph neural networks (EQGNNs) offer a potentially powerful method to process graph data. However, existing EQGNN models only consider the permutation symmetry of graphs, ...

Abstract: Recently, table structure recognition has achieved impressive progress with the help of deep graph models. Most of them exploit single visual cues of tabular elements or simply combine ...

This course surveys the use of optimization to design behavior. We will explore ways to represent policies including hand-designed parametric functions, basis functions, tables, and trajectory ...

In the field of evolutionary computation, it is common to compare different algorithms using a large test set, especially when the test involves function optimization [GW93]. However, the ...