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Certifying the safety or robustness of neural networks against input uncertainties and adversarial attacks is an emerging challenge in the area of safe machine learning and control.
G. Zames and P. Falb, “Stability conditions for systems with monotone and slope-restricted nonlinearities,” SIAM Journal on Control
1968
Earlier work this paper cites.
Siam, 1994
S. Boyd, L. El Ghaoui, E. Feron, and V. Balakrishnan, Linear matrix inequalities in system and control theory · 1994
Earlier work this paper cites.
V. Yakubovich, “S-procedure in nonlinear control theory,” Vestnick Leningrad Univ. Math
1997
Earlier work this paper cites.
A. Megretski and A. Rantzer, “System analysis via integral quadratic constraints,” IEEE Transactions on Automatic Control
1997
Earlier work this paper cites.
F. D’amato, M. A. Rotea, A. Megretski, and U. Jönsson, “New results for analysis of systems with repeated nonlinearities,” Automatica
2001
Earlier work this paper cites.
Prentice hall Upper Saddle River, NJ, 2002
H. K. Khalil and J. W. Grizzle, Nonlinear systems · 2002
Earlier work this paper cites.
V. V. Kulkarni and M. G. Safonov, “All multipliers for repeated monotone nonlinearities,” IEEE Transactions on Automatic Control
2002
Earlier work this paper cites.
Princeton University Press, 2009
A. Ben-Tal, L. El Ghaoui, and A. Nemirovski, Robust optimization · 2009
Earlier work this paper cites.
L. Pulina and A. Tacchella, “Challenging smt solvers to verify neural networks,” AI Communications
2012
Earlier work this paper cites.
2013
Earlier work this paper cites.
Springer Science & Business Media, 2013
Y. Nesterov, Introductory lectures on convex optimization: A basic course · 2013
Earlier work this paper cites.
2014
Earlier work this paper cites.
S. Zheng, Y. Song, T. Leung, and I. Goodfellow, “Improving the robustness of deep neural networks via stability training,” in Proceedings of the ieee conference on computer vision and pattern recognition
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
K. D. Julian, J. Lopez, J. S. Brush, M. P. Owen, and M. J. Kochenderfer, “Policy compression for aircraft collision avoidance systems,” in Digital Avionics Systems Conference (DASC), 2016 IEEE/AIAA 35th
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
N. Papernot, P. McDaniel, S. Jha, M. Fredrikson, Z. B. Celik, and A. Swami, “The limitations of deep learning in adversarial settings,” in 2016 IEEE European Symposium on Security and Privacy (EuroS&P)
2016
Cited alongside, same era.
S.-M. Moosavi-Dezfooli, A. Fawzi, and P. Frossard, “Deepfool: a simple and accurate method to fool deep neural networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
2016
Cited alongside, same era.
O. Bastani, Y. Ioannou, L. Lampropoulos, D. Vytiniotis, A. Nori, and A. Criminisi, “Measuring neural net robustness with constraints,” in Advances in neural information processing systems
2016
Cited alongside, same era.
S.-M. Moosavi-Dezfooli, A. Fawzi, O. Fawzi, and P. Frossard, “Universal adversarial perturbations,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2017
Cited alongside, same era.
W. Xiang, H.-D. Tran, and T. T. Johnson, “Output reachable set estimation and verification for multilayer neural networks,” IEEE transactions on neural networks and learning systems
2018
Later among the works it cites.
M. Mirman, T. Gehr, and M. Vechev, “Differentiable abstract interpretation for provably robust neural networks,” in International Conference on Machine Learning
2018
Later among the works it cites.
T. Gehr, M. Mirman, D. Drachsler-Cohen, P. Tsankov, S. Chaudhuri, and M. Vechev, “Ai2: Safety and robustness certification of neural networks with abstract interpretation,” in 2018 IEEE Symposium on Security and Privacy (SP)
2018
Later among the works it cites.
2018
Later among the works it cites.
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2017
Cited alongside, same era.
2017
Cited alongside, same era.
2017
Cited alongside, same era.
M. Hein and M. Andriushchenko, “Formal guarantees on the robustness of a classifier against adversarial manipulation,” in Advances in Neural Information Processing Systems
2017
Cited alongside, same era.
R. Ehlers, “Formal verification of piece-wise linear feed-forward neural networks,” in International Symposium on Automated Technology for Verification and Analysis
2017
Cited alongside, same era.
X. Huang, M. Kwiatkowska, S. Wang, and M. Wu, “Safety verification of deep neural networks,” in International Conference on Computer Aided Verification
2017
Cited alongside, same era.
G. Katz, C. Barrett, D. L. Dill, K. Julian, and M. J. Kochenderfer, “Reluplex: An efficient smt solver for verifying deep neural networks,” in International Conference on Computer Aided Verification
2017
Cited alongside, same era.
C.-H. Cheng, G. Nührenberg, and H. Ruess, “Maximum resilience of artificial neural networks,” in International Symposium on Automated Technology for Verification and Analysis
2017
Cited alongside, same era.
H. Zhang, T.-W. Weng, P.-Y. Chen, C.-J. Hsieh, and L. Daniel, “Efficient neural network robustness certification with general activation functions,” in Advances in neural information processing systems
2018
Later among the works it cites.
S. Wang, K. Pei, J. Whitehouse, J. Yang, and S. Jana, “Efficient formal safety analysis of neural networks,” in Advances in Neural Information Processing Systems
2018
Later among the works it cites.
S. Wang, K. Pei, J. Whitehouse, J. Yang, and S. Jana, “Formal security analysis of neural networks using symbolic intervals,” in 27th USENIX Security Symposium (USENIX Security 18)
2018
Later among the works it cites.
2018
Later among the works it cites.
A. Raghunathan, J. Steinhardt, and P. S. Liang, “Semidefinite relaxations for certifying robustness to adversarial examples,” in Advances in Neural Information Processing Systems
2018
Later among the works it cites.
2018
Later among the works it cites.
J. Su, D. V. Vargas, and K. Sakurai, “One pixel attack for fooling deep neural networks,” IEEE Transactions on Evolutionary Computation
2019
Closest in time.
H. Salman, G. Yang, H. Zhang, C.-J. Hsieh, and P. Zhang, “A convex relaxation barrier to tight robustness verification of neural networks,” in Advances in Neural Information Processing Systems
2019
Closest in time.
R. Ivanov, J. Weimer, R. Alur, G. J. Pappas, and I. Lee, “Verisig: Verifying safety properties of hybrid systems with neural network controllers,” in Proceedings of the 22nd ACM International Conference on Hybrid Systems: Computation and Control
2019
Closest in time.
M. Fazlyab, M. Morari, and G. J. Pappas, “Probabilistic verification and reachability analysis of neural networks: Convex relaxations,” in 2019 IEEE Conference on Decision and Control (CDC)
2019
Closest in time.
I. CVX Research, “CVX: Matlab software for disciplined convex programming, version 2.0.” http://cvxr.com/cvx , Aug. 2012
2020
Closest in time.