the four main areas of criticism below (reviewers referred to as R1-5)

We first thank the reviewers for their insightful comments which we have taken into careful consideration. If our work were to be evaluated using only performance metrics, this criticism would be fair. Learning paradigms for networks of'convex layers' have been shown to be effective (e.g. The key advance over standard SCNs is that we show how to perform non-linear computations in these systems. Standard SCNs such as in Boerlin et al (2013) are restricted to linear computations. It may seem surprising, but such layers are actually not well understood!