The robustness of deep neural networks has received significant interest recently, especially
when being deployed in safety-critical systems, as it is important to analyze how sensitive …

Deep neural networks (DNNs) are a state-of-the-art technology, capable of outstanding
performance in many key tasks. However, it is challenging to integrate DNNs into safety …

Data-driven, neural network (NN) based anomaly detection and predictive maintenance are
emerging as important research areas. NN-based analytics of time-series data provide …

Neural networks are successful in various applications but are also susceptible to
adversarial attacks. To show the safety of network classifiers, many verifiers have been …

Abstract Machine learning-based techniques have shown great promises in perception,
prediction, planning, and general decision-making for improving task performance of …

Deep neural networks have demonstrated impressive performance in a wide variety of
applications. However, deep neural networks are not perfect. In many cases, additional …

The advancement of Deep Neural Network (DNN) technologies and their verification
methodologies has not fully extended to the realm of time-series neural network (NN) …