EnergyNet: Energy-based Adaptive Structural Learning of Artificial Neural Network Architectures
Gus Kristiansen, Xavi Gonzalvo · arXiv (Cornell University) · 2017
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.