References

  1. QuantifyML GitHub. Available at https://github.com/muhammadusman93/quantifyml.
  2. Mahdieh Abbasi, Arezoo Rajabi, Christian Gagné & Rakesh B. Bobba (2020): Toward Adversarial Robustness by Diversity in an Ensemble of Specialized Deep Neural Networks. In: Cyril Goutte & Xiaodan Zhu: Advances in Artificial Intelligence. Springer International Publishing, Cham, pp. 1–14, doi:10.1007/978-3-030-47358-7_1.
  3. Rehan Abdul Aziz, Geoffrey Chu, Christian Muise & Peter James Stuckey (2015): #SAT: Projected Model Counting. In: SAT, doi:10.1007/978-3-319-24318-4_10.
  4. Joao Bastos (2007): Credit scoring with boosted decision trees. Available at https://mpra.ub.uni-muenchen.de/8156/1/MPRA_paper_8156.pdf.
  5. Armin Biere, Alessandro Cimatti, Edmund M. Clarke, Ofer Strichman & Yunshan Zhu (2003): Bounded model checking. Adv. Comput. 58, pp. 117–148, doi:10.1016/S0065-2458(03)58003-2.
  6. B. Bonakdarpour & S. S. Kulkarni (2007): Exploiting Symbolic Techniques in Automated Synthesis of Distributed Programs with Large State Space. In: ICDCS, doi:10.1109/ICDCS.2007.109.
  7. Supratik Chakraborty, Kuldeep S. Meel & Moshe Y. Vardi (2013): A Scalable Approximate Model Counter. In: Christian Schulte: Principles and Practice of Constraint Programming. Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 200–216, doi:10.1007/978-3-642-40627-0_18.
  8. Edmund M. Clarke, Daniel Kroening & Flavio Lerda (2004): A Tool for Checking ANSI-C Programs. In: Tools and Algorithms for the Construction and Analysis of Systems, 10th International Conference, TACAS 2004, Held as Part of the Joint European Conferences on Theory and Practice of Software, ETAPS 2004, Barcelona, Spain, March 29 - April 2, 2004, Proceedings, pp. 168–176, doi:10.1007/978-3-540-24730-2_15.
  9. Jeremy Cohen, Elan Rosenfeld & Zico Kolter (2019): Certified Adversarial Robustness via Randomized Smoothing. In: Kamalika Chaudhuri & Ruslan Salakhutdinov: Proceedings of the 36th International Conference on Machine Learning, Proceedings of Machine Learning Research 97. PMLR, pp. 1310–1320. Available at http://proceedings.mlr.press/v97/cohen19c.html.
  10. Patrice Godefroid & Sarfraz Khurshid (2002): Exploring Very Large State Spaces Using Genetic Algorithms. In: Joost-Pieter Katoen & Perdita Stevens: Tools and Algorithms for the Construction and Analysis of Systems. Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 266–280, doi:10.1007/3-540-46002-0_19.
  11. Carla P Gomes, Ashish Sabharwal & Bart Selman (2009): Model counting. In: Handbook of satisfiability. IOS press, pp. 633–654, doi:10.3233/978-1-58603-929-5-633.
  12. Ian Goodfellow, Yoshua Bengio & Aaron Courville (2016): Deep Learning. MIT Press. Available at http://www.deeplearningbook.org.
  13. Divya Gopinath, Guy Katz, Corina S. Păsăreanu & Clark Barrett (2018): DeepSafe: A Data-Driven Approach for Assessing Robustness of Neural Networks. In: Shuvendu K. Lahiri & Chao Wang: Automated Technology for Verification and Analysis. Springer International Publishing, Cham, pp. 3–19, doi:10.1007/978-3-030-01090-4_1.
  14. G. Hinton, L. Deng, D. Yu, G. E. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath & B. Kingsbury (2012): Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups. IEEE Signal Processing Magazine 29(6), pp. 82–97, doi:10.1109/MSP.2012.2205597.
  15. Xiaowei Huang, Daniel Kroening, Wenjie Ruan, James Sharp, Youcheng Sun, Emese Thamo, Min Wu & Xinping Yi (2020): A survey of safety and trustworthiness of deep neural networks: Verification, testing, adversarial attack and defence, and interpretability. Computer Science Review 37, pp. 100270, doi:10.1016/j.cosrev.2020.100270.
  16. Daniel Jackson (2002): Alloy: a lightweight object modelling notation. ACM Trans. Softw. Eng. Methodol. 11(2), doi:10.1145/505145.505149.
  17. 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: Computer Aided Verification - 29th International Conference, CAV 2017, Heidelberg, Germany, July 24-28, 2017, Proceedings, Part I, pp. 97–117, doi:10.1007/978-3-319-63387-9_5.
  18. Nikhil Ketkar (2017): Introduction to keras. In: Deep learning with Python. Springer, pp. 97–111, doi:10.1007/978-1-4842-2766-4_7.
  19. Alex Krizhevsky, Ilya Sutskever & Geoffrey E Hinton (2012): ImageNet Classification with Deep Convolutional Neural Networks. In: F. Pereira, C. J. C. Burges, L. Bottou & K. Q. Weinberger: Advances in Neural Information Processing Systems 25. Curran Associates, Inc., pp. 1097–1105, doi:10.1145/3065386.
  20. Daniel Kroening & Michael Tautschnig (2014): CBMC–C bounded model checker. In: International Conference on Tools and Algorithms for the Construction and Analysis of Systems. Springer, pp. 389–391, doi:10.1007/978-3-642-54862-8_26.
  21. Wen-Jia Kuo, Ruey-Feng Chang, Dar-Ren Chen & Cheng Chun Lee (2001): Data mining with decision trees for diagnosis of breast tumor in medical ultrasonic images. Breast cancer research and treatment 66(1), pp. 51–57, doi:10.1023/A:1010676701382.
  22. Jean-Marie Lagniez & Pierre Marquis (2019): A recursive algorithm for projected model counting. In: Proceedings of the AAAI Conference on Artificial Intelligence 33, pp. 1536–1543, doi:10.1609/aaai.v33i01.33011536.
  23. M. P. Owen, A. Panken, R. Moss, L. Alvarez & C. Leeper (2019): ACAS Xu: Integrated Collision Avoidance and Detect and Avoid Capability for UAS. In: 2019 IEEE/AIAA 38th Digital Avionics Systems Conference (DASC), pp. 1–10, doi:10.1109/DASC43569.2019.9081758.
  24. Nicolas Papernot, Patrick D. McDaniel, Somesh Jha, Matt Fredrikson, Z. Berkay Celik & Ananthram Swami (2016): The Limitations of Deep Learning in Adversarial Settings. In: EuroS&P, doi:10.1109/EuroSP.2016.36.
  25. Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss & Vincent Dubourg (2011): Scikit-learn: Machine learning in Python. the Journal of machine Learning research 12, pp. 2825–2830. Available at https://www.jmlr.org/papers/volume12/pedregosa11a/pedregosa11a.pdf.
  26. S Rasoul Safavian & David Landgrebe (1991): A survey of decision tree classifier methodology. IEEE transactions on systems, man, and cybernetics 21(3), pp. 660–674, doi:10.1109/21.97458.
  27. C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow & R. Fergus (2013): Intriguing Properties of Neural Networks. Available at http://arxiv.org/abs/1312.6199. Technical Report.
  28. Muhammad Usman, Wenxi Wang & Sarfraz Khurshid (2020): TestMC: Testing Model Counters using Differential and Metamorphic Testing. In: 2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE). IEEE, pp. 709–721, doi:10.1145/3324884.3416563.
  29. Muhammad Usman, Wenxi Wang, Marko Vasic, Kaiyuan Wang, Haris Vikalo & Sarfraz Khurshid (2020): A Study of the Learnability of Relational Properties: Model Counting Meets Machine Learning (MCML). PLDI 2020. Association for Computing Machinery, New York, NY, USA, pp. 10981111, doi:10.1145/3385412.3386015.
Comments and questions to: eptcs@eptcs.org
For website issues: webmaster@eptcs.org