Notes on Hierarchical Splines, DCLNs and i-theory
Tomaso Poggio, Lorenzo Rosasco, Amnon Shashua, Nadav Cohen, Fabio Anselmi · DSpace@MIT (Massachusetts Institute of Technology) · 2015
We define an extension of classical additive splines for multivariate function approximation that we call hierarchical splines. We show that the case of hierarchical, additive, piece-wise linear splines includes present-day Deep Convolutional Learning Networks (DCLNs) with linear rectifiers and pooling (sum or max). We discuss how these observations together with i-theory may provide a framework for a general theory of deep networks.