Artificial intelligence (AI) has been advancing at a fast pace and it is now poised for
deployment in a wide range of applications, such as autonomous systems, medical …

Recently, there has been a significant growth of interest in applying software engineering
techniques for the quality assurance of deep learning (DL) systems. One popular direction is …

The surge of interest in applications of deep neural networks has led to a surge of interest in
verification methods for such architectures. In summer 2020, the first international …

Most work on the formal verification of neural networks has focused on bounding the set of
outputs that correspond to a given set of inputs (for example, bounded perturbations of a …

Deep learning has become a promising programming paradigm in software development,
owing to its surprising performance in solving many challenging tasks. Deep neural …

Verifying and explaining the behavior of neural networks is becoming increasingly
important, especially when they are deployed in safety-critical applications. In this paper, we …

Robust pedestrian trajectory forecasting is crucial to developing safe autonomous vehicles.
Although previous works have studied adversarial robustness in the context of trajectory …

To alleviate the practical constraints for deploying deep neural networks (DNNs) on edge
devices, quantization is widely regarded as one promising technique. It reduces the …

As a new programming paradigm, deep neural networks (DNNs) have been increasingly
deployed in practice, but the lack of robustness hinders their applications in safety-critical …

Recently, Graph Neural Networks (GNNs) have been applied for scheduling jobs over
clusters, achieving better performance than hand-crafted heuristics. Despite their impressive …