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Neural networks are increasingly deployed in real-world safety-critical domains such as autonomous driving, aircraft collision avoidance, and malware detection.
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M. J. C. Ramon E. Moore, R. Baker Kearfott · 2009
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An abstraction-refinement approach to verification of artificial neural networks
L. Pulina and A. Tacchella · 2010
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A polynomial number of random points does not determine the volume of a convex body
R. Eldan · 2011
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Next-generation airborne collision avoidance system
M. J. Kochenderfer, J. E. Holland, and J. P. Chryssanthacopoulos · 2012
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On the number of response regions of deep feed forward networks with piece-wise linear activations
R. Pascanu, G. Montufar, and Y. Bengio · 2013
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Mobile-sandbox: having a deeper look into android applications
M. Spreitzenbarth, F. Freiling, F. Echtler, T. Schreck, and J. Hoffmann · 2013
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Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus · 2013
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Drebin: Effective and explainable detection of android malware in your pocket
D. Arp, M. Spreitzenbarth, M. Hubner, H. Gascon, K. Rieck, and C. Siemens · 2014
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On the number of linear regions of deep neural networks
G. F. Montufar, R. Pascanu, K. Cho, and Y. Bengio · 2014
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Explaining and harnessing adversarial examples
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2015
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Airborne collision avoidance system x
M. T. Notes · 2015
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Policy compression for aircraft collision avoidance systems
K. D. Julian, J. Lopez, J. S. Brush, M. P. Owen, and M. J. Kochenderfer · 2016
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Understanding neural networks through representation erasure
J. Li, W. Monroe, and D. Jurafsky · 2016
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End to end learning for self-driving cars
M. Bojarski, D. Del Testa, D. Dworakowski, B. Firner, B. Flepp, P. Goyal, L. D. Jackel, M. Monfort, U. Muller, J. Zhang, et al · 2017
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Formal verification of piece-wise linear feed-forward neural networks
R. Ehlers · 2017
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Deep neural networks as 0-1 mixed integer linear programs: A feasibility study
M. Fischetti and J. Jo · 2017
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Safety verification of deep neural networks
X. Huang, M. Kwiatkowska, S. Wang, and M. Wu · 2017
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Reluplex: An efficient smt solver for verifying deep neural networks
G. Katz, C. Barrett, D. Dill, K. Julian, and M. Kochenderfer · 2017
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Towards proving the adversarial robustness of deep neural networks
Training verified learners with learned verifiers
K. Dvijotham, S. Gowal, R. Stanforth, R. Arandjelovic, B. O’Donoghue, J. Uesato, and P. Kohli · 2018
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A dual approach to scalable verification of deep networks
K. Dvijotham, R. Stanforth, S. Gowal, T. Mann, and P. Kohli · 2018
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Ai 2: Safety and robustness certification of neural networks with abstract interpretation
T. Gehr, M. Mirman, D. Drachsler-Cohen, P. Tsankov, S. Chaudhuri, and M. Vechev · 2018
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Certified robustness to adversarial examples with differential privacy
M. Lecuyer, V. Atlidakis, R. Geambasu, H. Daniel, and S. Jana · 2018
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Differentiable abstract interpretation for provably robust neural networks
M. Mirman, T. Gehr, and M. Vechev · 2018
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G. Katz, C. Barrett, D. L. Dill, K. Julian, and M. J. Kochenderfer · 2017
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Understanding black-box predictions via influence functions
P. W. Koh and P. Liang · 2017
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Lower bounds on the robustness to adversarial perturbations
J. Peck, J. Roels, B. Goossens, and Y. Saeys · 2017
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Deepxplore: Automated whitebox testing of deep learning systems
K. Pei, Y. Cao, J. Yang, and S. Jana · 2017
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Towards practical verification of machine learning: The case of computer vision systems
K. Pei, Y. Cao, J. Yang, and S. Jana · 2017
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Learning important features through propagating activation differences
A. Shrikumar, P. Greenside, and A. Kundaje · 2017
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Evaluating robustness of neural networks with mixed integer programming
V. Tjeng, K. Xiao, and R. Tedrake · 2017
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Certified defenses against adversarial examples
A. Raghunathan, J. Steinhardt, and P. Liang · 2018
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DeepTest: Automated testing of deep-neural-network-driven autonomous cars
Y. Tian, K. Pei, S. Jana, and B. Ray · 2018
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Formal security analysis of neural networks using symbolic intervals
S. Wang, K. Pei, W. Justin, J. Yang, and S. Jana · 2018
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Towards fast computation of certified robustness for relu networks
T.-W. Weng, H. Zhang, H. Chen, Z. Song, C.-J. Hsieh, D. Boning, I. S. Dhillon, and L. Daniel · 2018
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Evaluating the robustness of neural networks: An extreme value theory approach
T.-W. Weng, H. Zhang, P.-Y. Chen, J. Yi, D. Su, Y. Gao, C.-J. Hsieh, and L. Daniel · 2018
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Provable defenses against adversarial examples via the convex outer adversarial polytope
E. Wong and J. Z. Kolter · 2018
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Scaling provable adversarial defenses
E. Wong, F. Schmidt, J. H. Metzen, and J. Z. Kolter · 2018
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Efficient neural network robustness certification with general activation functions
H. Zhang, T.-W. Weng, P.-Y. Chen, C.-J. Hsieh, and L. Daniel · 2018
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