  In this paper we present an approach for training deep generative models
solely based on solving determined systems of linear equations. A network that
uses this approach, called a StarNet, has the following desirable properties:
1) training requires no gradient as solution to the system of linear equations
is not stochastic, 2) is highly scalable when solving the system of linear
equations w.r.t the latent codes, and similarly for the parameters of the
model, and 3) it gives desirable least-square bounds for the estimation of
latent codes and network parameters within each layer.
