This paper presents the Neural Network Verification (NNV) software tool, a set-based
verification framework for deep neural networks (DNNs) and learning-enabled cyber …

Abstract Convolutional Neural Networks (CNN) have redefined state-of-the-art in many real-
world applications, such as facial recognition, image classification, human pose estimation …

Neural Networks (NNs) can provide major empirical performance improvements for closed-
loop systems, but they also introduce challenges in formally analyzing those systems' safety …

There has been an increasing interest in using neural networks in closed-loop control
systems to improve performance and reduce computational costs for on-line implementation …

Neural networks are increasingly being used as components in safety-critical applications,
for instance, as controllers in embedded systems. Their formal safety verification has made …

Multiple Constant Multiplication (MCM) over integers is a frequent operation arising in
embedded systems that require highly optimized hardware. An efficient way is to replace …

Neural Networks (NNs) can provide major empirical performance improvements for robotic
systems, but they also introduce challenges in formally analyzing those systems' safety …

Formal certification of Neural Networks (NNs) is crucial for ensuring their safety, fairness,
and robustness. Unfortunately, on the one hand, sound and complete certification algorithms …

This report presents the results of a friendly competition for formal verification of continuous
and hybrid systems with artificial intelligence (AI) components. Specifically, machine …

In this paper, we compute finite sample bounds for data-driven approximations of the
solution to stochastic reachability problems. Our approach uses a nonparametric technique …