185 citations · 657 across the 21 of their papers we have counts for
5 papers · 1 filter
Why and When Can Deep -- but Not Shallow -- Networks Avoid the Curse of Dimensionality: a Review
Tomaso Poggio, Hrushikesh Mhaskar, Lorenzo Rosasco +2
The paper characterizes classes of functions for which deep learning can be exponentially better than shallow learning. Deep convolutional networks are a special case of these cond…
Streaming Normalization: Towards Simpler and More Biologically-plausible Normalizations for Online and Recurrent Learning
Qianli Liao, Kenji Kawaguchi, Tomaso Poggio
We systematically explored a spectrum of normalization algorithms related to Batch Normalization (BN) and propose a generalized formulation that simultaneously solves two major lim…
View-tolerant face recognition and Hebbian learning imply mirror-symmetric neural tuning to head orientation
Joel Z. Leibo, Qianli Liao, Winrich Freiwald +2
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 rela…
Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex
Qianli Liao, Tomaso Poggio
We discuss relations between Residual Networks (ResNet), Recurrent Neural Networks (RNNs) and the primate visual cortex. We begin with the observation that a special type of shallo…
Learning Functions: When Is Deep Better Than Shallow
Hrushikesh Mhaskar, Qianli Liao, Tomaso Poggio
While the universal approximation property holds both for hierarchical and shallow networks, we prove that deep (hierarchical) networks can approximate the class of compositional f…