Connection- and Node-Sparse Deep Learning: Statistical Guarantees

Neural networks are becoming increasingly popular in applications, but a comprehensive mathematical understanding of their potentials and limitations is still missing. In this paper, we study the prediction accuracies of neural networks from a statistical point of view. In particular, we establish statistical prediction guarantees for deep learning with different types of sparsity-inducing regularization. Our bounds feature a mild dependence on network widths and depths, and, therefore, support the current trend toward wide and deep networks. The tools that we use in our derivations are uncommon in deep learning and, hence, might be of additional interest.