Testing and verification of neural-network-based safety-critical control software: A systematic literature review

5 October 2019

Papers citing "Testing and verification of neural-network-based safety-critical control software: A systematic literature review"

9 / 9 papers shown

Title
A Systematic Literature Review on Safety of the Intended Functionality for Automated Driving Systems Milin Patel Rolf Jung M. Khatun 69 0 0 04 Mar 2025
Beyond Confidence: Adaptive Abstention in Dual-Threshold Conformal Prediction for Autonomous System Perception Divake Kumar Nastaran Darabi Sina Tayebati A. R. Trivedi 74 0 0 11 Feb 2025
Compositional Inductive Invariant Based Verification of Neural Network Controlled Systems Yuhao Zhou S. Tripakis 21 1 0 17 Dec 2023
Toward Certification of Machine-Learning Systems for Low Criticality Airborne Applications Konstantin Dmitriev J. Schumann F. Holzapfel 13 20 0 28 Sep 2022
How to Certify Machine Learning Based Safety-critical Systems? A Systematic Literature Review Florian Tambon Gabriel Laberge Le An Amin Nikanjam Paulina Stevia Nouwou Mindom Y. Pequignot Foutse Khomh G. Antoniol E. Merlo François Laviolette 25 65 0 26 Jul 2021
DeepSafe: A Data-driven Approach for Checking Adversarial Robustness in Neural Networks D. Gopinath Guy Katz C. Păsăreanu Clark W. Barrett AAML 42 87 0 02 Oct 2017
Output Reachable Set Estimation and Verification for Multi-Layer Neural Networks Weiming Xiang Hoang-Dung Tran Taylor T. Johnson 88 292 0 09 Aug 2017
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks Guy Katz Clark W. Barrett D. Dill Kyle D. Julian Mykel Kochenderfer AAML 231 1,837 0 03 Feb 2017
Safety Verification of Deep Neural Networks Xiaowei Huang M. Kwiatkowska Sen Wang Min Wu AAML 180 932 0 21 Oct 2016