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We improve the effectiveness of propagation- and linear-optimization-based neural network verification algorithms with a new tightened convex relaxation for ReLU neurons.
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Karsten Scheibler, Leonore Winterer, Ralf Wimmer, and Bernd Becker · 2015
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Nicolas Papernot, Patrick McDaniel, Somesh Jha, Matt Fredrikson, Z. Berkay Celik, and Ananthram Swami · 2016
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Nicholas Carlini and David Wagner · 2017
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Chih-Hong Cheng, Georg Nührenberg, and Harald Ruess · 2017
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Xiaowei Huang, Marta Kwiatkowska, Sen Wang, and Min Wu · 2017
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Guy Katz, Clark Barrett, David L. Dill, Kyle Julian, and Mykel J. Kochenderfer · 2017
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An approach to reachability analysis for feed-forward ReLU neural networks
Alessio Lomuscio and Lalit Maganti · 2017
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Souradeep Dutta, Susmit Jha, Sriram Sankaranarayanan, and Ashish Tiwari · 2018
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Krishnamurthy Dvijotham, Robert Stanforth, Sven Gowal, Timothy A Mann, and Pushmeet Kohli · 2018
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Matteo Fischetti and Jason Jo · 2018
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Chen Liu, Mathieu Salzmann, and Sabine Süsstrunk · 2019
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Zhaoyang Lyu, Ching-Yun Ko, Zhifeng Kong, Ngai Wong, Dahua Lin, and Luca Daniel · 2019
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Ansgar Rössig · 2019
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Hadi Salman, Greg Yang, Huan Zhang, Cho-Jui Hsieh, and Pengchuan Zhang · 2019
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Beyond the single neuron convex barrier for neural network certification
Gagandeep Singh, Rupanshu Ganvir, Markus Püschel, and Martin Vechev · 2019
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An abstract domain for certifying neural networks
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Aditi Raghunathan, Jacob Steinhardt, and Percy S Liang · 2018
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Verifying neural networks with mixed integer programming
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Training for faster adversarial robustness verification via inducing ReLU stability
Kai Y. Xiao, Vincent Tjeng, Nur Muhammad Shafiullah, and Aleksander Madry · 2019
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Tightened convex relaxations for neural network robustness certification
Brendon G Anderson, Ziye Ma, Jingqi Li, and Somayeh Sojoudi · 2020
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