Abstract

We are inspired by an application in deep learning called the DropConnect method, which sparses edges in a complete bipartite graph (connections) to avoid overfitting, and its application to experimental design to improve estimation accuracy. We propose a combinatorial problem called Spanning Bipartite Block Design (SBBD) and show how to construct designs that satisfy the combinatorial requirements of SBBD.