Deep Neural Networks are known for impressive results in a wide range of applications,
being responsible for many advances in technology over the past few years. However …

Analyzing and understanding how abstract representations of data are formed inside deep
neural networks is a complex task. Among the different methods that have been developed …

Deep neural networks are becoming omnipresent in reason of their growing popularity in
media and their daily use. However, their global complexity makes them hard to understand …

Recently, deep learning has gained exceptional popularity due to its outstanding
performances in many machine learning and artificial intelligence applications. Among …

The performance of deep learning models is dependent on the precise configuration of
many layers and parameters. However, there are currently few systematic guidelines for how …

Machine Learning with Deep Neural Networks (DNNs) has become a successful tool in
solving tasks across various fields of application. However, the complexity of DNNs makes it …

Picasso is a free open-source (Eclipse Public License) web application written in Python for
rendering standard visualizations useful for analyzing convolutional neural networks …

Recent years have produced great advances in training large, deep neural networks
(DNNs), including notable successes in training convolutional neural networks (convnets) to …

Over the last decade, Convolutional Neural Networks (CNN) saw a tremendous surge in
performance. However, understanding what a network has learned still proves to be a …

Deep neural networks are able to solve tasks across a variety of domains and modalities of
data. Despite many empirical successes, we lack the ability to clearly understand and …