  We present E NERGY N ET , a new framework for analyzing and building
artificial neural network architectures. Our approach adaptively learns the
structure of the networks in an unsupervised manner. The methodology is based
upon the theoretical guarantees of the energy function of restricted Boltzmann
machines (RBM) of infinite number of nodes. We present experimental results to
show that the final network adapts to the complexity of a given problem.
