Now showing items 1-5 of 5
Learning Real and Boolean Functions: When Is Deep Better Than Shallow
(Center for Brains, Minds and Machines (CBMM), arXiv, 2016-03-08)
We describe computational tasks - especially in vision - that correspond to compositional/hierarchical functions. While the universal approximation property holds both for hierarchical and shallow networks, we prove that ...
Theory I: Why and When Can Deep Networks Avoid the Curse of Dimensionality?
(Center for Brains, Minds and Machines (CBMM), arXiv, 2016-11-23)
[formerly titled "Why and When Can Deep – but Not Shallow – Networks Avoid the Curse of Dimensionality: a Review"] The paper reviews and extends an emerging body of theoretical results on deep learning including the ...
Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex
(Center for Brains, Minds and Machines (CBMM), arXiv, 2016-04-12)
We discuss relations between Residual Networks (ResNet), Recurrent Neural Networks (RNNs) and the primate visual cortex. We begin with the observation that a shallow RNN is exactly equivalent to a very deep ResNet with ...
View-tolerant face recognition and Hebbian learning imply mirror-symmetric neural tuning to head orientation
(Center for Brains, Minds and Machines (CBMM), arXiv, 2016-06-03)
The primate brain contains a hierarchy of visual areas, dubbed the ventral stream, which rapidly computes object representations that are both specific for object identity and relatively robust against identity-preserving ...
Streaming Normalization: Towards Simpler and More Biologically-plausible Normalizations for Online and Recurrent Learning
(Center for Brains, Minds and Machines (CBMM), arXiv, 2016-10-19)
We systematically explored a spectrum of normalization algorithms related to Batch Normalization (BN) and propose a generalized formulation that simultaneously solves two major limitations of BN: (1) online learning and ...