New hybrid kernel architectures for deep learning

Abstract

In this work we explore the possibilities of combining neural network architectures and kernel methods by introducing hybrid kernel blocks. We present hybrid architectures which can be trained as traditional neural networks and introduce novel training and regularization methodologies for them.