Title: Convexification of Neural Graph,
Abstract: Traditionally, most complex intelligence architectures are extremely
non-convex, which could not be well performed by convex optimization. However,
this paper decomposes complex structures into three types of nodes: operators,
algorithms and functions. Iteratively, propagating from node to node along
edge, we prove that "regarding the tree-structured neural graph, it is nearly
convex in each variable, when the other variables are fixed." In fact, the
non-convex properties stem from circles and functions, which could be
transformed to be convex with our proposed \textit{\textbf{scale mechanism}}.
Experimentally, we justify our theoretical analysis by two practical
applications.