ETH Robustness Analyzer for Neural Networks (ERAN) github repository.
https://github.com/eth-sri/eran, last accessed 2021-04-10.
François Chollet:
Keras.
https://keras.io/, last accessed 2021-02-19.
Martin Davis, George Logemann & Donald Loveland (1962):
A Machine Program for Theorem-Proving.
Communications of the ACM 5(7),
pp. 394397,
doi:10.1145/368273.368557.
Martin Fränzle, Christian Herde, Stefan Ratschan, Tobias Schubert & Tino Teige (2007):
Efficient solving of large non-linear arithmetic constraint systems with complex boolean structure.
Journal of Satisfiability, Boolean Modeling and Computation,
pp. 209–236,
doi:10.3233/SAT190012.
Timon Gehr, Matthew Mirman, Dana Drachsler-Cohen, Petar Tsankov, Swarat Chaudhuri & Martin Vechev (2018):
AI2: Safety and Robustness Certification of Neural Networks with Abstract Interpretation.
In: 2018 IEEE Symposium on Security and Privacy (SP),
pp. 3–18,
doi:10.1109/SP.2018.00058.
Christian Herde (2011):
Efficient Solving of Large Arithmetic Constraint Systems with Complex Boolean Structure: Integration of DPLL and Interval Constraint Solving - ETCS model.
Vieweg+Teubner Research,
doi:10.1007/978-3-8348-9949-1.
Xiaowei Huang, Marta Kwiatkowska, Sen Wang & Min Wu (2017):
Safety Verification of Deep Neural Networks.
In: Rupak Majumdar & Viktor Kunčak: Computer Aided Verification.
Springer,
pp. 3–29,
doi:10.1007/978-3-319-63387-9_1.
Radoslav Ivanov, James Weimer, Rajeev Alur, George J. Pappas & Insup Lee (2019):
Verisig: Verifying Safety Properties of Hybrid Systems with Neural Network Controllers.
In: Proceedings of the 22nd ACM International Conference on Hybrid Systems: Computation and Control,
HSCC '19.
Association for Computing Machinery,
New York, NY, USA,
pp. 169178,
doi:10.1145/3302504.3311806.
Guy Katz, Clark W. Barrett, David L. Dill, Kyle Julian & Mykel J. Kochenderfer (2017):
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks.
In: Rupak Majumdar & Viktor Kunčak: Computer Aided Verification.
Springer,
pp. 97–117,
doi:10.1007/978-3-319-63387-9_5.
Guy Katz, Derek A. Huang, Duligur Ibeling, Kyle Julian, Christopher Lazarus, Rachel Lim, Parth Shah, Shantanu Thakoor, Haoze Wu, Aleksandar Zelji\'c, David L. Dill, Mykel J. Kochenderfer & Clark Barrett (2019):
The Marabou Framework for Verification and Analysis of Deep Neural Networks.
In: Isil Dillig & Serdar Tasiran: Computer Aided Verification.
Springer International Publishing,
Cham,
pp. 443–452,
doi:10.1007/978-3-030-25540-4_26.
Diederik P. Kingma & Jimmy Ba (2017):
Adam: A Method for Stochastic Optimization.
Available at https://arxiv.org/abs/1412.6980.
Alex Krizhevsky (2009):
Learning multiple layers of features from tiny images.
Technical Report.
Department of Computer Science, University of Toronto.
Available at https://www.cs.toronto.edu/~kriz/cifar.html.
Alex Krizhevsky, Ilya Sutskever & Geoffrey Hinton (2012):
ImageNet Classification with Deep Convolutional Neural Networks.
Neural Information Processing Systems 25,
doi:10.1145/3065386.
Yann LeCun, Corinna Cortes & Christopher J. C. Burges:
MNIST.
http://yann.lecun.com/exdb/mnist/, last accessed 2021-07-08.
Guido Manfredi & Yannick Jestin (2016):
An introduction to ACAS Xu and the challenges ahead.
In: 2016 IEEE/AIAA 35th Digital Avionics Systems Conference (DASC),
pp. 1–9,
doi:10.1109/DASC.2016.7778055.
Leonardo de Moura & Nikolaj Bjørner (2008):
Z3: An Efficient SMT Solver.
In: C. R. Ramakrishnan & Jakob Rehof: Tools and Algorithms for the Construction and Analysis of Systems.
Springer,
pp. 337–340,
doi:10.1007/978-3-540-78800-3_24.
Luca Pulina & Armando Tacchella (2010):
An Abstraction-Refinement Approach to Verification of Artificial Neural Networks.
In: Tayssir Touili, Byron Cook & Paul Jackson: Computer Aided Verification.
Springer,
pp. 243–257,
doi:10.1007/978-3-642-14295-6_24.
Francesca Rossi, Peter van Beek & Toby Walsh (2006):
Handbook of Constraint Programming (Foundations of Artificial Intelligence).
Elsevier Science Inc.,
USA.
Karsten Scheibler, Leonore Winterer, Ralf Wimmer & Bernd Becker (2015):
Towards Verification of Artificial Neural Networks.
In: Ulrich Heinkel, Daniel Kriesten & Marko Rößler: Methoden und Beschreibungssprachen zur Modellierung und Verifikation von Schaltungen und Systemen.
Technische Universität Chemnitz Professur Schaltkreis- und Systementwurf,
pp. 30–40.
Siddharth Sharma, Simone Sharma & Anidhya Athaiya (2020):
Activation Functions in Neural Networks.
International Journal of Engineering Applied Sciences and Technology 4,
pp. 310–316,
doi:10.33564/IJEAST.2020.v04i12.054.
Weiming Xiang, Patrick Musau, Ayana A. Wild, Diego Manzanas Lopez, Nathaniel Hamilton, Xiaodong Yang, Joel Rosenfeld & Taylor T. Johnson (2018):
Verification for Machine Learning, Autonomy, and Neural Networks Survey.
Available at https://arxiv.org/pdf/1810.01989.pdf.
eptcs@eptcs.org