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We present E NERGY N ET , a new framework for analyzing and buildingartificial neural network architectures. Our approach adaptively learns thestructure of the networks in an unsupervised manner. The methodology is basedupon the theoretical guarantees of the energy function of restricted Boltzmannmachines (RBM) of infinite number of nodes. We present experimental results toshow that the final network adapts to the complexity of a given problem.

EnergyNet: Energy-based Adaptive Structural Learning of Artificial Neural Network Architectures