355 citations · 1.6k across the 54 of their papers we have counts for
8 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…
Deep vs. shallow networks : An approximation theory perspective
Hrushikesh Mhaskar, Tomaso Poggio
The paper briefy reviews several recent results on hierarchical architectures for learning from examples, that may formally explain the conditions under which Deep Convolutional Ne…
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…
Invariant recognition drives neural representations of action sequences
Andrea Tacchetti, Leyla Isik, Tomaso Poggio
Recognizing the actions of others from visual stimuli is a crucial aspect of human visual perception that allows individuals to respond to social cues. Humans are able to identify…
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…
Group Invariant Deep Representations for Image Instance Retrieval
Olivier Morère, Antoine Veillard, Jie Lin +3
Most image instance retrieval pipelines are based on comparison of vectors known as global image descriptors between a query image and the database images. Due to their success in…