Contemporary deep neural networks exhibit impressive results on practical problems. These networks generalize well although their inherent capacity may extend significantly beyond the number of training examples. We analyze this behavior in the context of deep, infinite neural networks. We show that deep infinite layers are naturally aligned with Gaussian processes and kernel methods, and devise stochastic kernels that encode the information of these networks. We show that stability results apply despite the size, offering an explanation for their empirical success.
This approach can be kernelized and the study of infinite layer neural networks has resulted further theory aligning neural networks with Gaussian processes and kernel methods REF .
Steps Toward Deep Kernel Methods from Infinite Neural Networks
ArXiv
ArXiv
Computer Science