Now showing items 1-4 of 4
On Invariance and Selectivity in Representation Learning
(Center for Brains, Minds and Machines (CBMM), arXiv, 2015-03-23)
We discuss data representation which can be learned automatically from data, are invariant to transformations, and at the same time selective, in the sense that two points have the same representation only if they are one ...
I-theory on depth vs width: hierarchical function composition
(Center for Brains, Minds and Machines (CBMM), 2015-12-29)
Deep learning networks with convolution, pooling and subsampling are a special case of hierar- chical architectures, which can be represented by trees (such as binary trees). Hierarchical as well as shallow networks can ...
Deep Convolutional Networks are Hierarchical Kernel Machines
(Center for Brains, Minds and Machines (CBMM), arXiv, 2015-08-05)
We extend i-theory to incorporate not only pooling but also rectifying nonlinearities in an extended HW module (eHW) designed for supervised learning. The two operations roughly correspond to invariance and selectivity, ...
Notes on Hierarchical Splines, DCLNs and i-theory
(Center for Brains, Minds and Machines (CBMM), 2015-09-29)
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 ...