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DeepCert: Verification of Contextually Relevant Robustness for Neural
  Network Image Classifiers

DeepCert: Verification of Contextually Relevant Robustness for Neural Network Image Classifiers

International Conference on Computer Safety, Reliability, and Security (SAFECOMP), 2021
2 March 2021
Colin Paterson
Haoze Wu
John M. Grese
R. Calinescu
C. Păsăreanu
Clark W. Barrett
    AAML
ArXiv (abs)PDFHTML

Papers citing "DeepCert: Verification of Contextually Relevant Robustness for Neural Network Image Classifiers"

10 / 10 papers shown
Learning Run-time Safety Monitors for Machine Learning Components
Learning Run-time Safety Monitors for Machine Learning Components
Ozan Vardal
Richard Hawkins
Colin Paterson
Chiara Picardi
Daniel Omeiza
Lars Kunze
Ibrahim Habli
253
1
0
23 Jun 2024
Relational DNN Verification With Cross Executional Bound Refinement
Relational DNN Verification With Cross Executional Bound RefinementInternational Conference on Machine Learning (ICML), 2024
Debangshu Banerjee
Gagandeep Singh
AAML
330
8
0
16 May 2024
Marabou 2.0: A Versatile Formal Analyzer of Neural Networks
Marabou 2.0: A Versatile Formal Analyzer of Neural NetworksInternational Conference on Computer Aided Verification (CAV), 2024
Haoze Wu
Omri Isac
Aleksandar Zeljić
Teruhiro Tagomori
M. Daggitt
...
Min Wu
Min Zhang
Ekaterina Komendantskaya
Guy Katz
Clark W. Barrett
403
83
0
25 Jan 2024
Towards Scenario-based Safety Validation for Autonomous Trains with Deep
  Generative Models
Towards Scenario-based Safety Validation for Autonomous Trains with Deep Generative ModelsInternational Conference on Computer Safety, Reliability, and Security (SAFECOMP), 2023
Thomas Decker
Ananta R. Bhattarai
Michael Lebacher
233
5
0
16 Oct 2023
Tighter Abstract Queries in Neural Network Verification
Tighter Abstract Queries in Neural Network VerificationLogic Programming and Automated Reasoning (LPAR), 2022
Elazar Cohen
Y. Elboher
Clark W. Barrett
Guy Katz
398
9
0
23 Oct 2022
Utilizing Class Separation Distance for the Evaluation of Corruption Robustness of Machine Learning Classifiers
Utilizing Class Separation Distance for the Evaluation of Corruption Robustness of Machine Learning Classifiers
George J. Siedel
S. Vock
Andrey Morozov
Stefan Voss
170
4
0
27 Jun 2022
Toward Certified Robustness Against Real-World Distribution Shifts
Toward Certified Robustness Against Real-World Distribution Shifts
Haoze Wu
Teruhiro Tagomori
Avi Schwarzschild
Fengjun Yang
Nikolai Matni
George Pappas
Hamed Hassani
C. Păsăreanu
Clark W. Barrett
AAMLOOD
268
23
0
08 Jun 2022
Efficient Neural Network Analysis with Sum-of-Infeasibilities
Efficient Neural Network Analysis with Sum-of-InfeasibilitiesInternational Conference on Tools and Algorithms for Construction and Analysis of Systems (TACAS), 2022
Haoze Wu
Aleksandar Zeljić
Guy Katz
Clark W. Barrett
AAML
310
36
0
19 Mar 2022
An Abstraction-Refinement Approach to Verifying Convolutional Neural
  Networks
An Abstraction-Refinement Approach to Verifying Convolutional Neural NetworksAutomated Technology for Verification and Analysis (ATVA), 2022
Matan Ostrovsky
Clark W. Barrett
Guy Katz
305
31
0
06 Jan 2022
Scaling Polyhedral Neural Network Verification on GPUs
Scaling Polyhedral Neural Network Verification on GPUs
Christoph Müller
F. Serre
Gagandeep Singh
Markus Püschel
Martin Vechev
AAML
336
65
0
20 Jul 2020
1
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