In the past few years, significant progress has been made on deep neural networks (DNNs)
in achieving human-level performance on several long-standing tasks. With the broader …

Deep neural networks are at the forefront of machine learning research. However, despite
achieving impressive performance on complex tasks, they can be very sensitive: Small …

Artificial intelligence (AI) is currently being utilized in a wide range of sophisticated
applications, but the outcomes of many AI models are challenging to comprehend and trust …

Large language models (LLMs) have exploded a new heatwave of AI for their ability to
engage end-users in human-level conversations with detailed and articulate answers across …

Concolic testing combines program execution and symbolic analysis to explore the
execution paths of a software program. In this paper, we develop the first concolic testing …

Deep neural networks (DNNs) have a wide range of applications, and software employing
them must be thoroughly tested, especially in safety-critical domains. However, traditional …

Abstract Convolutional Neural Networks (CNN) have redefined state-of-the-art in many real-
world applications, such as facial recognition, image classification, human pose estimation …

Machine learning (ML) sees an increasing prevalence of being used in the Internet of Things
(IoT)-based smart grid. However, the trustworthiness of ML is a severe issue that must be …

Autonomous, or self-driving, cars are emerging as the solution to several problems primarily
caused by humans on roads, such as accidents and traffic congestion. However, those …

Understanding the spatial arrangement and nature of real-world objects is of paramount
importance to many complex engineering tasks, including autonomous navigation. Deep …