Deep Learning (DL) systems have received exponential growth in popularity and have
become ubiquitous in our everyday life. Such systems are built on top of popular DL …

Deep Learning (DL) library bugs affect downstream DL applications, emphasizing the need
for reliable systems. Generating valid input programs for fuzzing DL libraries is challenging …

Batch Normalization is a key component in almost all state-of-the-art image classifiers, but it
also introduces practical challenges: it breaks the independence between training examples …

Deep Learning (DL) has gained wide attention in recent years. Meanwhile, bugs in DL
systems can lead to serious consequences, and may even threaten human lives. As a result …

Bugs in Deep Learning (DL) libraries may affect almost all downstream DL applications, and
it is crucial to ensure the quality of such systems. It is challenging to generate valid input …

Deep learning (DL) has been applied widely, and the quality of DL system becomes crucial,
especially for safety-critical applications. Existing work mainly focuses on the quality …

The rapid development of Machine Learning (ML) has demonstrated superior performance
in many areas, such as computer vision and video and speech recognition. It has now been …

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 …

In the past decade, Deep Learning (DL) systems have been widely deployed in various
application domains to facilitate our daily life, eg, natural language processing, healthcare …

Machine translation software has become heavily integrated into our daily lives due to the
recent improvement in the performance of deep neural networks. However, machine …