Algorithms for Verifying Deep Neural Networks

作者: Changliu Liu , Tomer Arnon , Christopher Lazarus , Christopher Strong , Clark Barrett

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摘要: Deep neural networks are widely used for nonlinear function approximation, with applications ranging from computer vision to control. Although these networks involve the …

参考文章(67)
Yunong Zhang, Zhijun Zhang, Yunong Zhang, Zhijun Zhang, Dual Neural Network Repetitive Motion Planning and Control of Redundant Robot Manipulators. pp. 33- 56 ,(2013) , 10.1007/978-3-642-37518-7_4
Clark Barrett, Roberto Sebastiani, Sanjit A Seshia, Cesare Tinelli, Satisfiability Modulo Theories Handbook of Satisfiability. pp. 305- 343 ,(2018) , 10.1007/978-3-319-10575-8_11
Hayhurst Kelly J, Veerhusen Dan S, Chilenski John J, Rierson Leanna K, None, A Practical Tutorial on Modified Condition/Decision Coverage NASA Langley Technical Report Server. ,(2001)
Yann Lecun, D Touresky, G Hinton, T Sejnowski, A theoretical framework for back-propagation IEEE Computer Society Press. pp. 21- 28 ,(1988)
Jeff Bezanson, Alan Edelman, Stefan Karpinski, Viral B. Shah, Julia: A Fresh Approach to Numerical Computing Siam Review. ,vol. 59, pp. 65- 98 ,(2017) , 10.1137/141000671
David McClosky, Christopher Manning, Mihai Surdeanu, John Bauer, Jenny Finkel, Steven Bethard, The Stanford CoreNLP Natural Language Processing Toolkit Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations. pp. 55- 60 ,(2014) , 10.3115/V1/P14-5010
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, Demis Hassabis, None, Human-level control through deep reinforcement learning Nature. ,vol. 518, pp. 529- 533 ,(2015) , 10.1038/NATURE14236
Nicolas Papernot, Patrick McDaniel, Somesh Jha, Matt Fredrikson, Z. Berkay Celik, Ananthram Swami, The Limitations of Deep Learning in Adversarial Settings ieee european symposium on security and privacy. pp. 372- 387 ,(2016) , 10.1109/EUROSP.2016.36