Deep Learning is the most widely used tool in the contemporary field of computer vision. Its
ability to accurately solve complex problems is employed in vision research to learn deep …

As we seek to deploy machine learning models beyond virtual and controlled domains, it is
critical to analyze not only the accuracy or the fact that it works most of the time, but if such a …

Deep Learning (DL) algorithms based on artificial neural networks have achieved
remarkable success and are being extensively applied in a variety of application domains …

Deep learning is at the heart of the current rise of artificial intelligence. In the field of
computer vision, it has become the workhorse for applications ranging from self-driving cars …

Public resources and services (eg, datasets, training platforms, pre-trained models) have
been widely adopted to ease the development of Deep Learning-based applications …

It is commonly believed that networks cannot be both accurate and robust, that gaining
robustness means losing accuracy. It is also generally believed that, unless making …

Learning to reject is a special kind of self-awareness (the ability to know what you do not
know), which is an essential factor for humans to become smarter. Although machine …

Machine Learning methods are playing a vital role in combating ever-evolving threats in the
cybersecurity domain. Explanation methods that shed light on the decision process of black …

Deep neural networks are vulnerable to adversarial attacks, in which imperceptible
perturbations to their input lead to erroneous network predictions. This phenomenon has …

The rapid development of artificial intelligence (AI) and breakthroughs in Internet of Things
(IoT) technologies have driven the innovation of advanced autonomous driving systems …