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Deep neural networks are revolutionizing the way complex systems are developed.
Parallelization Techniques for Verifying Neural Networks, 2020
H. Wu, A. Ozdemir, A. Zeljić, A. Irfan, K. Julian, D. Gopinath, S. Fouladi, G. Katz, C. Păsăreanu, and C. Barrett · 2004
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Simplifying Loop Invariant Generation Using Splitter Predicates
R. Sharma, I. Dillig, T. Dillig, and A. Aiken · 2011
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Intriguing Properties of Neural Networks, 2013
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus · 2013
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Deep Learning
I. Goodfellow, Y. Bengio, and A. Courville · 2016
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Learning to Diagnose with LSTM Recurrent Neural Networks
Z. Lipton, D. Kale, C. Elkan, and R. Wetzel · 2016
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Decidability of Inferring Inductive Invariants
O. Padon, N. Immerman, S. Shoham, A. Karbyshev, and M. Sagiv · 2016
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Provably Minimally-Distorted Adversarial Examples, 2017
N. Carlini, G. Katz, C. Barrett, and D. Dill · 2017
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Maximum Resilience of Artificial Neural Networks
C.-H. Cheng, G. Nührenberg, and H. Ruess · 2017
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Houdini: Fooling Deep Structured Visual and Speech Recognition Models with Adversarial Examples
M. Cisse, Y. Adi, N. Neverova, and J. Keshet · 2017
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Formal Verification of Piece-Wise Linear Feed-Forward Neural Networks
R. Ehlers · 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
G. Katz, C. Barrett, D. Dill, K. Julian, and M. Kochenderfer · 2017
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An Approach to Reachability Analysis for Feed-Forward ReLU Neural Networks, 2017
A. Lomuscio and L. Maganti · 2017
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Verifying Properties of Binarized Deep Neural Networks, 2017
N. Narodytska, S. Kasiviswanathan, L. Ryzhyk, M. Sagiv, and T. Walsh · 2017
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Counterexample-Guided Approach to Finding Numerical Invariants
T. Nguyen, T. Antonopoulos, A. Ruef, and M. Hicks · 2017
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Generalized End-to-End Loss for Speaker Verification, 2017
L. Wan, Q. Wang, A. Papir, and I. Lopez-Moreno · 2017
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A Unified View of Piecewise Linear Neural Network Verification
R. Bunel, I. Turkaslan, P. Torr, P. Kohli, and P. Mudigonda · 2018
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Formal Security Analysis of Neural Networks using Symbolic Intervals
S. Wang, K. Pei, J. Whitehouse, J. Yang, and S. Jana · 2018
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Verification of RNN-Based Neural Agent-Environment Systems
M. Akintunde, A. Kevorchian, A. Lomuscio, and E. Pirovano · 2019
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The Marabou Framework for Verification and Analysis of Deep Neural Networks
G. Katz, D. Huang, D. Ibeling, K. Julian, C. Lazarus, R. Lim, P. Shah, S. Thakoor, H. Wu, A. Zeljić, D. Dill, M. Kochenderfer, and C. Barrett · 2019
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Verifying Deep-RL-Driven Systems
Y. Kazak, C. Barrett, G. Katz, and M. Schapira · 2019
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POPQORN: Quantifying Robustness of Recurrent Neural Networks
C. Ko, Z. Lyu, T. Weng, L. Daniel, N. Wong, and D. Lin · 2019
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Evaluating Robustness of Neural Networks with Mixed Integer Programming
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Verification of Binarized Neural Networks via Inter-Neuron Factoring
C.-H. Cheng, G. Nührenberg, C.-H. Huang, and H. Ruess · 2018
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, 2018
J. Devlin, M. Chang, K. Lee, and K. Toutanova · 2018
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AI2: Safety and Robustness Certification of Neural Networks with Abstract Interpretation
T. Gehr, M. Mirman, D. Drachsler-Cohen, E. Tsankov, S. Chaudhuri, and M. Vechev · 2018
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DeepSafe: A Data-driven Approach for Assessing Robustness of Neural Networks
D. Gopinath, G. Katz, C. Pǎsǎreanu, and C. Barrett · 2018
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Fooling End-to-End Speaker Verification with Adversarial Examples
F. Kreuk, Y. Adi, M. Cisse, and J. Keshet · 2018
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Toward Scalable Verification for Safety-Critical Deep Networks, 2018
L. Kuper, G. Katz, J. Gottschlich, K. Julian, C. Barrett, and M. Kochenderfer · 2018
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Learning Loop Invariants for Program Verification
X. Si, H. Dai, M. Raghothaman, M. Naik, and L. Song · 2018
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V. Tjeng, K. Xiao, and R. Tedrake · 2019
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CSTR VCTK Corpus: English Multi-speaker Corpus for CSTR Voice Cloning Toolkit, 2019
J. Yamagishi, C. Veaux, and K. MacDonald · 2019
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An Abstraction-Based Framework for Neural Network Verification
Y. Elboher, J. Gottschlich, and G. Katz · 2020
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Simplifying Neural Networks using Formal Verification
S. Gokulanathan, A. Feldsher, A. Malca, C. Barrett, and G. Katz · 2020
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Minimal Modifications of Deep Neural Networks using Verification
B. Goldberger, Y. Adi, J. Keshet, and G. Katz · 2020
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RnnVerify, 2020
Y. Jacoby, C. Barrett, and G. Katz · 2020
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Verification of Recurrent Neural Networks for Cognitive Tasks via Reachability Analysis
H. Zhang, M. Shinn, A. Gupta, A. Gurfinkel, N. Le, and N. Narodytska · 2020
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