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.
Golowich et al. study the sample complexity of deep neural networks and present upper bounds on the Rademacher complexity of the neural networks in terms of the norm of the weight matrix in each layer REF .
Size-Independent Sample Complexity of Neural Networks
ArXiv
ArXiv
Computer Science Mathematics