Recently, many applications have begun to employ deep neural networks (DNN), such as
image recognition and safety-critical applications, for more accurate results. One of the most …

Deep neural networks (DNN) have achieved unprecedented success in numerous machine
learning tasks in various domains. However, the existence of adversarial examples leaves …

Deep neural networks (DNNs) are key components for the implementation of autonomy in
systems that operate in highly complex and unpredictable environments (self-driving cars …

In recent years, various domains have been influenced by the rapid growth of machine
learning. Autonomous driving is an area that has tremendously developed in parallel with …

Deep neural networks (DNNs) have accomplished impressive success in various
applications, including autonomous driving perception tasks, in recent years. On the other …

Deep neural networks (DNNs) have transformed several artificial intelligence research
areas including computer vision, speech recognition, and natural language processing …

Adversarial attacks on Deep Neural Networks represent a critical challenge in the adoption
of DNNs in critical applications. However,-and in spite of its great need,-there is significant …

Boosted by the evolution of machine learning technology, large amount of data and
advanced computing system, neural networks have achieved state-of-the-art performance …

Nowadays, autonomous driving has attracted much attention from both industry and
academia. Convolutional neural network (CNN) is a key component in autonomous driving …

The popularity of adapting deep neural networks (DNNs) in solving hard problems has
increased substantially. Specifically, in the field of computer vision, DNNs are becoming a …