With the advancement in artificial intelligence (AI) and machine learning (ML) techniques,
researchers are striving towards employing these techniques for advancing clinical practice …

Artificial vision has often been described as one of the key remaining challenges to be
solved before machines can act intelligently. Recent developments in a branch of machine …

Machine learning methods have been remarkably successful for a wide range of application
areas in the extraction of essential information from data. An exciting and relatively recent …

Deep learning has achieved tremendous success in recent years. In simple words, deep
learning uses the composition of many nonlinear functions to model the complex …

Finding sensory stimuli that drive neurons optimally is central to understanding information
processing in the brain. However, optimizing sensory input is difficult due to the …

What specific features should visual neurons encode, given the infinity of real-world images
and the limited number of neurons available to represent them? We investigated neuronal …

This paper presents the philosophy, design and feature-set of Neural Network Distiller, an
open-source Python package for DNN compression research. Distiller is a library of DNN …

Biological visual systems have evolved to process natural scenes. A full understanding of
visual cortical functions requires a comprehensive characterization of how neuronal …

Deep convolutional neural networks (CNNs) have been successful in many tasks in
machine vision, however, millions of weights in the form of thousands of convolutional filters …

We use topological data analysis (TDA) to study how data transforms as it passes through
successive layers of a deep neural network (DNN). We compute the persistent homology of …