AdaNet: Adaptive Structural Learning of Artificial Neural Networks
Proceedings of the 34th International Conference on Machine Learning, PMLR 70:874-883, 2017.
We present a new 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, so that the final network architecture provably adapts to the complexity of any given problem.