23 citations · 69 across the 9 of their papers we have counts for
8 papers
Compression of Deep Neural Networks for Image Instance Retrieval
Vijay Chandrasekhar, Jie Lin, Qianli Liao +4
Image instance retrieval is the problem of retrieving images from a database which contain the same object. Convolutional Neural Network (CNN) based descriptors are becoming the do…
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…
Unsupervised learning of clutter-resistant visual representations from natural videos
Qianli Liao, Joel Z. Leibo, Tomaso Poggio
Populations of neurons in inferotemporal cortex (IT) maintain an explicit code for object identity that also tolerates transformations of object appearance e.g., position, scale, v…
Learning An Invariant Speech Representation
Georgios Evangelopoulos, Stephen Voinea, Chiyuan Zhang +2
Recognition of speech, and in particular the ability to generalize and learn from small sets of labelled examples like humans do, depends on an appropriate representation of the ac…