Title: Size-Independent Sample Complexity of Neural Networks,
Abstract: We study the sample complexity of learning neural networks, by providing new
bounds on their Rademacher complexity assuming norm constraints on the
parameter matrix of each layer. Compared to previous work, these complexity
bounds have improved dependence on the network depth, and under some additional
assumptions, are fully independent of the network size (both depth and width).
These results are derived using some novel techniques, which may be of
independent interest.