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Concerned with the reliability of neural networks, researchers have developed verification techniques to prove their robustness.
The complexity of theorem-proving procedures
Stephen A Cook · 1971
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Chaff: Engineering an efficient SAT solver
Matthew W Moskewicz, Conor F Madigan, Ying Zhao, Lintao Zhang, and Sharad Malik · 2001
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An extensible SAT-solver
Niklas Eén and Niklas Sörensson · 2003
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Towards an optimal cnf encoding of boolean cardinality constraints
Carsten Sinz · 2005
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A lightweight component caching scheme for satisfiability solvers
Knot Pipatsrisawat and Adnan Darwiche · 2007
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Z3: An efficient smt solver
Leonardo De Moura and Nikolaj Bjørner · 2008
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Cardinality networks and their applications
Roberto Asín, Robert Nieuwenhuis, Albert Oliveras, and Enric Rodríguez-Carbonell · 2009
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Handbook of satisfiability , volume 185
Armin Biere, Marijn Heule, and Hans van Maaren · 2009
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Conflict-driven clause learning SAT solvers
Joao Marques-Silva, Inês Lynce, and Sharad Malik · 2009
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Extending SAT solvers to cryptographic problems
Mate Soos, Karsten Nohl, and Claude Castelluccia · 2009
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Lynx: A programmatic SAT solver for the RNA-folding problem
Vijay Ganesh, Charles W O’donnell, Mate Soos, Srinivas Devadas, Martin C Rinard, and Armando Solar-Lezama · 2012
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A cardinality solver: more expressive constraints for free
Mark H Liffiton and Jordyn C Maglalang · 2012
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Estimating or propagating gradients through stochastic neurons for conditional computation
Yoshua Bengio, Nicholas Léonard, and Aaron Courville · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna Estrach, Dumitru Erhan, Ian Goodfellow, and Robert Fergus · 2014
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Learning both weights and connections for efficient neural network
Song Han, Jeff Pool, John Tran, and William Dally · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Towards verification of artificial neural networks
Karsten Scheibler, Leonore Winterer, Ralf Wimmer, and Bernd Becker · 2015
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Deep Learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
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Binarized neural networks
Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio · 2016
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Xnor-net: Imagenet classification using binary convolutional neural networks
Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, and Ali Farhadi · 2016
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Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients
Shuchang Zhou, Yuxin Wu, Zekun Ni, Xinyu Zhou, He Wen, and Yuheng Zou · 2016
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SAT competition 2016: Recent developments
Tomás Balyo, Marijn JH Heule, and Matti Jarvisalo · 2017
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Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
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Maximum resilience of artificial neural networks
Chih-Hong Cheng, Georg Nührenberg, and Harald Ruess · 2017
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Formal verification of piece-wise linear feed-forward neural networks
Ruediger Ehlers · 2017
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Safety verification of deep neural networks
Xiaowei Huang, Marta Kwiatkowska, Sen Wang, and Min Wu · 2017
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
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Differentiable abstract interpretation for provably robust neural networks
Matthew Mirman, Timon Gehr, and Martin Vechev · 2018
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Verifying properties of binarized deep neural networks
Nina Narodytska, Shiva Kasiviswanathan, Leonid Ryzhyk, Mooly Sagiv, and Toby Walsh · 2018
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Semidefinite relaxations for certifying robustness to adversarial examples
Aditi Raghunathan, Jacob Steinhardt, and Percy S Liang · 2018
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Towards fast computation of certified robustness for ReLU networks
Lily Weng, Huan Zhang, Hongge Chen, Zhao Song, Cho-Jui Hsieh, Luca Daniel, Duane Boning, and Inderjit Dhillon · 2018
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Efficient neural network robustness certification with general activation functions
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Cited alongside, same era.
Reluplex: An efficient smt solver for verifying deep neural networks
Guy Katz, Clark Barrett, David L Dill, Kyle Julian, and Mykel J Kochenderfer · 2017
Cited alongside, same era.
An approach to reachability analysis for feed-forward relu neural networks
Alessio Lomuscio and Lalit Maganti · 2017
Cited alongside, same era.
High performance binary neural networks on the Xeon+FPGA™platform
Duncan JM Moss, Eriko Nurvitadhi, Jaewoong Sim, Asit Mishra, Debbie Marr, Suchit Subhaschandra, and Philip HW Leong · 2017
Cited alongside, same era.
Provable defenses against adversarial examples via the convex outer adversarial polytope
Eric Wong and J Zico Kolter · 2017
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Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Anish Athalye, Nicholas Carlini, and David Wagner · 2018
Cited alongside, same era.
On the Glucose SAT solver
Gilles Audemard and Laurent Simon · 2018
Cited alongside, same era.
Huan Zhang, Tsui-Wei Weng, Pin-Yu Chen, Cho-Jui Hsieh, and Luca Daniel · 2018
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Quantitative verification of neural networks and its security applications
Teodora Baluta, Shiqi Shen, Shweta Shinde, Kuldeep S Meel, and Prateek Saxena · 2019
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Back to simplicity: How to train accurate bnns from scratch?
Joseph Bethge, Haojin Yang, Marvin Bornstein, and Christoph Meinel · 2019
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin · 2019
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Combinatorial attacks on binarized neural networks
Elias B Khalil, Amrita Gupta, and Bistra Dilkina · 2019
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PyTorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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A convex relaxation barrier to tight robustness verification of neural networks
Hadi Salman, Greg Yang, Huan Zhang, Cho-Jui Hsieh, and Pengchuan Zhang · 2019
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Verifying binarized neural networks by angluin-style learning
Andy Shih, Adnan Darwiche, and Arthur Choi · 2019
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Evaluating robustness of neural networks with mixed integer programming
Vincent Tjeng, Kai Y. Xiao, and Russ Tedrake · 2019
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Training for faster adversarial robustness verification via inducing reLU stability
Kai Y. Xiao, Vincent Tjeng, Nur Muhammad (Mahi) Shafiullah, and Aleksander Madry · 2019
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Learn to relax: Integrating 0-1 integer linear programming with pseudo-boolean conflict-driven search
Jo Devriendt, Ambros Gleixner, and Jakob Nordström · 2020
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Exploiting verified neural networks via floating point numerical error
Kai Jia and Martin Rinard · 2020
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Certifying joint adversarial robustness for model ensembles
Mainuddin Ahmad Jonas and David Evans · 2020
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In search for a SAT-friendly binarized neural network architecture
Nina Narodytska, Hongce Zhang, Aarti Gupta, and Toby Walsh · 2020
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Compact and efficient encodings for planning in factored state and action spaces with learned binarized neural network transition models
Buser Say and Scott Sanner · 2020
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On adaptive attacks to adversarial example defenses
Florian Tramer, Nicholas Carlini, Wieland Brendel, and Aleksander Madry · 2020
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