AdaNet: Adaptive Structural Learning of Artificial Neural Networks

We present a new theoretical framework for analyzing and learning artificial neural networks. Our approach simultaneously and adaptively learns both the structure of the network as well as its weights. The methodology is based upon and accom- panied by strong data-dependent theoretical learning guarantees, so that the final network architecture provably adapts to the complexity of any given problem.