Neural network pruning---the task of reducing the size of a network by removing parameters--
-has been the subject of a great deal of work in recent years. We provide a meta-analysis of …

Pruning large neural networks while maintaining their performance is often desirable due to
the reduced space and time complexity. In existing methods, pruning is done within an …

Structured pruning of filters or neurons has received increased focus for compressing
convolutional neural networks. Most existing methods rely on multi-stage optimizations in a …

The deployment of deep convolutional neural networks (CNNs) in many real world
applications is largely hindered by their high computational cost. In this paper, we propose a …

The success of CNNs in various applications is accompanied by a significant increase in the
computation and parameter storage costs. Recent efforts toward reducing these overheads …

Deploying convolutional neural networks (CNNs) on mobile devices is difficult due to the
limited memory and computation resources. We aim to design efficient neural networks for …

Object recognition is an important task for improving the ability of visual systems to perform
complex scene understanding. Recently, the Exponential Linear Unit (ELU) has been …

We demonstrate that modern image recognition methods based on artificial neural networks
can recover hidden information from images protected by various forms of obfuscation. The …

We use the scattering network as a generic and fixed initialization of the first layers of a
supervised hybrid deep network. We show that early layers do not necessarily need to be …

We propose a general framework for increasing local stability of Artificial Neural Nets
(ANNs) using Robust Optimization (RO). We achieve this through an alternating …