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 accompanied by strong data-dependent theoretical learning guarantees. We present some preliminary results to show that the final network architecture adapts to the complexity of a given problem.