In 2006, Geoffrey Hinton proposed the concept of training “Deep Neural Networks (DNNs)”
and an improved model training method to break the bottleneck of neural network …

Context: Advancements in machine learning (ML) lead to a shift from the traditional view of
software development, where algorithms are hard-coded by humans, to ML systems …

AI-based systems are software systems with functionalities enabled by at least one AI
component (eg, for image-, speech-recognition, and autonomous driving). AI-based systems …

The rapid growth of applying Machine Learning (ML) in different domains, especially in
safety-critical areas, increases the need for reliable ML components, ie, a software …

Deep learning is transforming most areas of science and technology, including electron
microscopy. This review paper offers a practical perspective aimed at developers with …

Significant interest in applying Deep Neural Network (DNN) has fueled the need to support
engineering of software that uses DNNs. Repairing software that uses DNNs is one such …

Deep learning (DL) becomes increasingly pervasive, being used in a wide range of software
applications. These software applications, named as DL based software (in short as DL …

Deep Neural Networks (DNNs) are becoming an integral part of most software systems.
Previous work has shown that DNNs have bugs. Unfortunately, existing debugging …

Enabled by a rich ecosystem of Machine Learning (ML) libraries, programming using
learned models, ie, Software-2.0, has gained substantial adoption. However, we do not …

Deep learning (DL) is moving its step into a growing number of mobile software applications.
These software applications, named as DL based mobile applications (abbreviated as …