@inproceedings{706d23f102ed443e9ed22691faa9083a,
title = "Demo: The Neural Network Verification (NNV) Tool",
abstract = "NNV (Neural Network Verification) is a framework for the verification of deep neural networks (DNNs) and learning-enabled cyber-physical systems (CPS) inspired by a collection of reachability algorithms that make use of a variety of set representations such as the star set. NNV supports exact and over-approximate reachability algorithms used to verify the safety and robustness of feed-forward neural networks (FFNNs). These two analysis schemes are also used for learning enabled CPS, i.e., closed-loop systems, and particularly in neural network control systems with linear models and FFNN controllers with piecewise-linear activation functions. Additionally, NNV supports over-approximate analysis for nonlinear plant models by combining the star set analysis used for FFNNs with the zonotope-based analysis for nonlinear plant dynamics provided by CORA. This demo paper demonstrates NNV's capabilities by considering a case study of the verification of a learning-enabled adaptive cruise control system.",
author = "Tran, {Hoang Dung} and Lopez, {Diego Manzanas} and Xiaodong Yang and Patrick Musau and Nguyen, {Luan Viet} and Weiming Xiang and Stanley Bak and Johnson, {Taylor T.}",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE Workshop on Design Automation for CPS and IoT, DESTION 2020 ; Conference date: 21-04-2020",
year = "2020",
month = apr,
doi = "10.1109/DESTION50928.2020.00010",
language = "English (US)",
series = "Proceedings - 2020 IEEE Workshop on Design Automation for CPS and IoT, DESTION 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "21--22",
booktitle = "Proceedings - 2020 IEEE Workshop on Design Automation for CPS and IoT, DESTION 2020",
}