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

We present an historical overview about the connections between the analysis of risk and
the control of autonomous systems. We offer two main contributions. Our first contribution is …

Reachability analysis is a fundamental problem in verification that checks for a given model
and set of initial states if the system will reach a given set of unsafe states. Its importance lies …

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 …

A method is presented to analyze the stability of feedback systems with neural network
controllers. Two stability theorems are given to prove asymptotic stability and to compute an …

It is quite challenging to ensure the safety of reinforcement learning (RL) agents in an
unknown and stochastic environment under hard constraints that require the system state …

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 …

Abstract This paper presents Verisig 2.0, a verification tool for closed-loop systems with
neural network (NN) controllers. We focus on NNs with tanh/sigmoid activations and develop …

Deep learning methods can be used to produce control policies, but certifying their safety is
challenging. The resulting networks are nonlinear and often very large. In response to this …

This article addresses the problem of verifying the safety of autonomous systems with neural
network (NN) controllers. We focus on NNs with sigmoid/tanh activations and use the fact …