Tutorial: Safe, Secure, and Trustworthy Artificial Intelligence (AI) via Formal Verification of Neural Networks and Autonomous Cyber-Physical Systems (CPS) with NNV-Reference-Cited by-同舟云学术

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Tutorial: Safe, Secure, and Trustworthy Artificial Intelligence (AI) via Formal Verification of Neural Networks and Autonomous Cyber-Physical Systems (CPS) with NNV

Published:2024-06-24 Issue: Volume:96 Page:65-66
ISSN:
Container-title:2024 54th Annual IEEE/IFIP International Conference on Dependable Systems and Networks - Supplemental Volume (DSN-S)
language:
Short-container-title:

Author:

Johnson Taylor T.¹,Lopez Diego Manzanas²,Tran Hoang-Dung³

Affiliation:

1. Vanderbilt University,Computer Science,Nashville,TN

2. Vanderbilt University,Institute for Software Integrated Systems,Nashville,TN

3. University of Nebraska Lincoln,Computer Science and Engineering,Lincoln,NE

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10647056/10647057/10647060.pdf?arnumber=10647060

Reference15 articles.

1. The Fallacy of AI Functionality

2. NNV: The Neural Network Verification Tool for Deep Neural Networks and Learning-Enabled Cyber-Physical Systems

3. NNV 2.0: The Neural Network Verification Tool

4. First three years of the international verification of neural networks competition (VNN-COMP)

5. ARCH-COMP23 Category Report: Artificial Intelligence and Neural Network Control Systems (AINNCS) for Continuous and Hybrid Systems Plants

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