Abstract Machine learning models are increasingly deployed in mission-critical settings such
as vehicles, but unfortunately, these models can fail in complex ways. To prevent errors, ML …

An increasing number of software applications incorporate machine learning (ML) solutions
for cognitive tasks that statistically mimic human behaviors. To test such software …

The reliability of software that has a Deep Neural Network (DNN) as a component is urgently
important today given the increasing number of critical applications being deployed with …

With the increasing adoption of deep learning (DL) in various applications, developers often
reuse models by, for example, performing model conversion among frameworks to raise …

Deep neural networks (DNNs) are increasingly being adopted for sensing and control
functions in a variety of safety and mission-critical systems such as self-driving cars …

There is increasing interest in the application of machine learning (ML) technologies to
safety-critical cyber-physical systems, with the promise of increased levels of autonomy due …

Due to the increasing usage of Deep Neural Network (DNN) based autonomous driving
systems (ADS) where erroneous or unexpected behaviours can lead to catastrophic …

Abstract Deep Neural Networks (DNNs) are increasingly deployed in safety-critical
applications including autonomous vehicles and medical diagnostics. To reduce the …

Machine learning (ML) cloud APIs enable developers to easily incorporate learning
solutions into software systems. Unfortunately, ML APIs are challenging to use correctly and …

The tremendous advancements in deep learning techniques have empowered question
answering (QA) systems with the capability of dealing with various tasks. Many commercial …