Title: Deep Learning Is Composite Kernel Learning. Abstract: Recent works have connected deep learning and kernel methods. In this paper, we show that architectural choices such as convolutional layers with pooling, skip connections, make deep learning a composite kernel learning method, where the kernel is a (architecture dependent) composition of base kernels: even before training, standard deep networks have in-built structural properties that ensure their success. In particular, we build on the recently developed `neural path' framework that characterises the role of gates/masks in fully connected deep networks with ReLU activations. 