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

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

5 October 2019
Jin Zhang
Jingyue Li
ArXivPDFHTML

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

27 / 27 papers shown
Title
A Systematic Literature Review on Safety of the Intended Functionality for Automated Driving Systems
A Systematic Literature Review on Safety of the Intended Functionality for Automated Driving Systems
Milin Patel
Rolf Jung
M. Khatun
92
0
0
04 Mar 2025
Beyond Confidence: Adaptive Abstention in Dual-Threshold Conformal Prediction for Autonomous System Perception
Beyond Confidence: Adaptive Abstention in Dual-Threshold Conformal Prediction for Autonomous System Perception
Divake Kumar
Nastaran Darabi
Sina Tayebati
A. R. Trivedi
154
0
0
11 Feb 2025
Software Engineering Challenges of Deep Learning
Software Engineering Challenges of Deep Learning
Anders Arpteg
B. Brinne
L. Crnkovic-Friis
J. Bosch
66
174
0
29 Oct 2018
Experimental Resilience Assessment of An Open-Source Driving Agent
Experimental Resilience Assessment of An Open-Source Driving Agent
A. Rubaiyat
Yongming Qin
H. Alemzadeh
45
44
0
17 Jul 2018
Toward Scalable Verification for Safety-Critical Deep Networks
Toward Scalable Verification for Safety-Critical Deep Networks
L. Kuper
Guy Katz
Justin Emile Gottschlich
Kyle D. Julian
Clark W. Barrett
Mykel Kochenderfer
86
40
0
18 Jan 2018
Towards Proving the Adversarial Robustness of Deep Neural Networks
Towards Proving the Adversarial Robustness of Deep Neural Networks
Guy Katz
Clark W. Barrett
D. Dill
Kyle D. Julian
Mykel J. Kochenderfer
AAML
OOD
49
118
0
08 Sep 2017
On a Formal Model of Safe and Scalable Self-driving Cars
On a Formal Model of Safe and Scalable Self-driving Cars
Shai Shalev-Shwartz
Shaked Shammah
Amnon Shashua
38
736
0
21 Aug 2017
Failing to Learn: Autonomously Identifying Perception Failures for
  Self-driving Cars
Failing to Learn: Autonomously Identifying Perception Failures for Self-driving Cars
M. Ramanagopal
Cyrus Anderson
Ram Vasudevan
Matthew Johnson-Roberson
52
104
0
30 Jun 2017
Interpretability via Model Extraction
Interpretability via Model Extraction
Osbert Bastani
Carolyn Kim
Hamsa Bastani
FAtt
47
129
0
29 Jun 2017
Real Time Image Saliency for Black Box Classifiers
Real Time Image Saliency for Black Box Classifiers
P. Dabkowski
Y. Gal
62
586
0
22 May 2017
Parseval Networks: Improving Robustness to Adversarial Examples
Parseval Networks: Improving Robustness to Adversarial Examples
Moustapha Cissé
Piotr Bojanowski
Edouard Grave
Yann N. Dauphin
Nicolas Usunier
AAML
133
800
0
28 Apr 2017
Interpretable Explanations of Black Boxes by Meaningful Perturbation
Interpretable Explanations of Black Boxes by Meaningful Perturbation
Ruth C. Fong
Andrea Vedaldi
FAtt
AAML
74
1,514
0
11 Apr 2017
Learning Important Features Through Propagating Activation Differences
Learning Important Features Through Propagating Activation Differences
Avanti Shrikumar
Peyton Greenside
A. Kundaje
FAtt
148
3,848
0
10 Apr 2017
Right for the Right Reasons: Training Differentiable Models by
  Constraining their Explanations
Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations
A. Ross
M. C. Hughes
Finale Doshi-Velez
FAtt
113
585
0
10 Mar 2017
Axiomatic Attribution for Deep Networks
Axiomatic Attribution for Deep Networks
Mukund Sundararajan
Ankur Taly
Qiqi Yan
OOD
FAtt
149
5,920
0
04 Mar 2017
Compositional Falsification of Cyber-Physical Systems with Machine
  Learning Components
Compositional Falsification of Cyber-Physical Systems with Machine Learning Components
T. Dreossi
Alexandre Donzé
Sanjit A. Seshia
AAML
77
230
0
02 Mar 2017
Detecting Adversarial Samples from Artifacts
Detecting Adversarial Samples from Artifacts
Reuben Feinman
Ryan R. Curtin
S. Shintre
Andrew B. Gardner
AAML
84
892
0
01 Mar 2017
On Detecting Adversarial Perturbations
On Detecting Adversarial Perturbations
J. H. Metzen
Tim Genewein
Volker Fischer
Bastian Bischoff
AAML
59
947
0
14 Feb 2017
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
Guy Katz
Clark W. Barrett
D. Dill
Kyle D. Julian
Mykel Kochenderfer
AAML
297
1,849
0
03 Feb 2017
Cyber-Physical Systems Security -- A Survey
Cyber-Physical Systems Security -- A Survey
Abdulmalik Humayed
Jingqiang Lin
Fengjun Li
Bo Luo
41
780
0
17 Jan 2017
Safety Verification of Deep Neural Networks
Safety Verification of Deep Neural Networks
Xiaowei Huang
Marta Kwiatkowska
Sen Wang
Min Wu
AAML
200
935
0
21 Oct 2016
Measuring Neural Net Robustness with Constraints
Measuring Neural Net Robustness with Constraints
Osbert Bastani
Yani Andrew Ioannou
Leonidas Lampropoulos
Dimitrios Vytiniotis
A. Nori
A. Criminisi
AAML
59
423
0
24 May 2016
Learning Deep Features for Discriminative Localization
Learning Deep Features for Discriminative Localization
Bolei Zhou
A. Khosla
Àgata Lapedriza
A. Oliva
Antonio Torralba
SSL
SSeg
FAtt
210
9,280
0
14 Dec 2015
Continuous control with deep reinforcement learning
Continuous control with deep reinforcement learning
Timothy Lillicrap
Jonathan J. Hunt
Alexander Pritzel
N. Heess
Tom Erez
Yuval Tassa
David Silver
Daan Wierstra
252
13,174
0
09 Sep 2015
Understanding Deep Image Representations by Inverting Them
Understanding Deep Image Representations by Inverting Them
Aravindh Mahendran
Andrea Vedaldi
FAtt
99
1,959
0
26 Nov 2014
Intriguing properties of neural networks
Intriguing properties of neural networks
Christian Szegedy
Wojciech Zaremba
Ilya Sutskever
Joan Bruna
D. Erhan
Ian Goodfellow
Rob Fergus
AAML
209
14,831
1
21 Dec 2013
Visualizing and Understanding Convolutional Networks
Visualizing and Understanding Convolutional Networks
Matthew D. Zeiler
Rob Fergus
FAtt
SSL
400
15,825
0
12 Nov 2013
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