References

  1. Michael Akintunde, Alessio Lomuscio, Lalit Maganti & Edoardo Pirovano (2018): Reachability analysis for neural agent-environment systems. In: Sixteenth International Conference on Principles of Knowledge Representation and Reasoning.
  2. Michael E Akintunde, Andreea Kevorchian, Alessio Lomuscio & Edoardo Pirovano (2019): Verification of RNN-Based Neural Agent-Environment Systems. In: Proceedings of the 33th AAAI Conference on Artificial Intelligence (AAAI19). Honolulu, HI, USA. AAAI Press, doi:10.1609/aaai.v33i01.33016006.
  3. Greg Anderson, Shankara Pailoor, Isil Dillig & Swarat Chaudhuri (2019): Optimization and abstraction: a synergistic approach for analyzing neural network robustness. In: Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation, pp. 731–744, doi:10.1145/3314221.3314614.
  4. Anurag Arnab, Ondrej Miksik & Philip HS Torr (2018): On the robustness of semantic segmentation models to adversarial attacks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 888–897, doi:10.1109/CVPR.2018.00099.
  5. M. Chen, X. Shi, Y. Zhang, D. Wu & M. Guizani (2017): Deep Features Learning for Medical Image Analysis with Convolutional Autoencoder Neural Network. IEEE Transactions on Big Data, pp. 1–1, doi:10.1109/TBDATA.2017.2717439.
  6. Z. Chen, C. K. Yeo, B. S. Lee & C. T. Lau (2018): Autoencoder-based network anomaly detection. In: 2018 Wireless Telecommunications Symposium (WTS), pp. 1–5, doi:10.1109/WTS.2018.8363930.
  7. Souradeep Dutta, Susmit Jha, Sriram Sankaranarayanan & Ashish Tiwari (2018): Learning and verification of feedback control systems using feedforward neural networks. IFAC-PapersOnLine 51(16), pp. 151–156, doi:10.1016/j.ifacol.2018.08.026.
  8. Souradeep Dutta, Susmit Jha, Sriram Sankaranarayanan & Ashish Tiwari (2018): Output range analysis for deep feedforward neural networks. In: NASA Formal Methods Symposium. Springer, pp. 121–138, doi:10.1007/978-3-319-77935-5_9.
  9. Krishnamurthy Dj Dvijotham, Robert Stanforth, Sven Gowal, Chongli Qin, Soham De & Pushmeet Kohli (2020): Efficient neural network verification with exactness characterization. In: Uncertainty in Artificial Intelligence. PMLR, pp. 497–507.
  10. Ruediger Ehlers (2017): Formal verification of piece-wise linear feed-forward neural networks. In: International Symposium on Automated Technology for Verification and Analysis. Springer, pp. 269–286, doi:10.1007/978-3-319-68167-2_19.
  11. Hatem M Elattar, Hamdy K Elminir & Alaa Mohamed Riad (2019): Conception and implementation of a data-driven prognostics algorithm for safety–critical systems. Soft Computing 23(10), pp. 3365–3382, doi:10.1007/s00500-017-2995-7.
  12. Peter M. Full, Fabian Isensee, Paul F. Jäger & Klaus Maier-Hein (2020): Studying Robustness of Semantic Segmentation under Domain Shift in cardiac MRI, doi:10.1007/978-3-030-68107-4_24. ArXiv:2011.07592.
  13. Timon Gehr, Matthew Mirman, Dana Drachsler-Cohen, Petar Tsankov, Swarat Chaudhuri & Martin Vechev (2018): Ai 2: Safety and robustness certification of neural networks with abstract interpretation. In: Security and Privacy (SP), 2018 IEEE Symposium on, doi:10.1109/SP.2018.00058.
  14. Ian J Goodfellow, Jonathon Shlens & Christian Szegedy (2014): Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572, doi:10.48550/arXiv.1412.6572.
  15. Xiaowei Huang, Marta Kwiatkowska, Sen Wang & Min Wu (2017): Safety verification of deep neural networks. In: International Conference on Computer Aided Verification. Springer, pp. 3–29, doi:10.1007/978-3-319-63387-9_1.
  16. Guy Katz, Clark Barrett, David L Dill, Kyle Julian & Mykel J Kochenderfer (2017): Reluplex: An efficient SMT solver for verifying deep neural networks. In: International Conference on Computer Aided Verification. Springer, pp. 97–117, doi:10.1007/978-3-319-63387-9_5.
  17. Guy Katz, Derek A Huang, Duligur Ibeling, Kyle Julian, Christopher Lazarus, Rachel Lim, Parth Shah, Shantanu Thakoor, Haoze Wu & Aleksandar Zelji\'c (2019): The marabou framework for verification and analysis of deep neural networks. In: International Conference on Computer Aided Verification. Springer, pp. 443–452, doi:10.1007/978-3-030-25540-4_26.
  18. Igor Khmelnitsky, Daniel Neider, Rajarshi Roy, Benoît Barbot, Benedikt Bollig, Alain Finkel, Serge Haddad, Martin Leucker & Lina Ye (2020): Property-Directed Verification of Recurrent Neural Networks. arXiv preprint arXiv:2009.10610, doi:10.48550/arXiv.2009.10610.
  19. Marvin Klingner, Andreas Bar & Tim Fingscheidt (2020): Improved Noise and Attack Robustness for Semantic Segmentation by Using Multi-Task Training With Self-Supervised Depth Estimation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, doi:10.1109/CVPRW50498.2020.00168.
  20. Panagiotis Kouvaros & Alessio Lomuscio (2018): Formal verification of cnn-based perception systems. arXiv preprint arXiv:1811.11373, doi:10.48550/arXiv.1811.11373.
  21. Alessio Lomuscio & Lalit Maganti (2017): An approach to reachability analysis for feed-forward relu neural networks. arXiv preprint arXiv:1706.07351, doi:10.48550/arXiv.1706.07351.
  22. Shervin Minaee, Yuri Boykov, Fatih Porikli, Antonio Plaza, Nasser Kehtarnavaz & Demetri Terzopoulos (2020): Image Segmentation Using Deep Learning: A Survey, doi:10.48550/arXiv.2001.05566. ArXiv:2001.05566.
  23. Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi & Pascal Frossard (2016): Deepfool: a simple and accurate method to fool deep neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2574–2582, doi:10.1109/CVPR.2016.282.
  24. Nauman Munir, Jinhyun Park, Hak-Joon Kim, Sung-Jin Song & Sung-Sik Kang (2020): Performance enhancement of convolutional neural network for ultrasonic flaw classification by adopting autoencoder. NDT & E International 111, pp. 102218, doi:10.1016/j.ndteint.2020.102218.
  25. Gabriel Oliveira, Claas Bollen, Wolfram Burgard & Thomas Brox (2017): Efficient and robust deep networks for semantic segmentation. The International Journal of Robotics Research 37, pp. 027836491771054, doi:10.1177/0278364917710542.
  26. Luca Pulina & Armando Tacchella (2010): An abstraction-refinement approach to verification of artificial neural networks. In: International Conference on Computer Aided Verification. Springer, pp. 243–257, doi:10.1007/978-3-642-14295-6_24.
  27. Markus Ringnér (2008): What is principal component analysis?. Nature biotechnology 26(3), pp. 303–304, doi:10.1038/nbt0308-303.
  28. Mayu Sakurada & Takehisa Yairi (2014): Anomaly detection using autoencoders with nonlinear dimensionality reduction. In: Proceedings of the MLSDA 2014 2nd Workshop on Machine Learning for Sensory Data Analysis, pp. 4–11, doi:10.1145/2689746.2689747.
  29. Divya Saxena & Vaskar Raychoudhury (2017): Design and verification of an NDN-based safety-critical application: A case study with smart healthcare. ieee transactions on systems, man, and cybernetics: systems 49(5), pp. 991–1005, doi:10.1109/TSMC.2017.2723843.
  30. Gagandeep Singh, Timon Gehr, Matthew Mirman, Markus Püschel & Martin Vechev (2018): Fast and effective robustness certification. In: Advances in Neural Information Processing Systems, pp. 10825–10836.
  31. Gagandeep Singh, Timon Gehr, Markus Püschel & Martin Vechev (2019): An abstract domain for certifying neural networks. Proceedings of the ACM on Programming Languages 3(POPL), pp. 41, doi:10.1145/3290354.
  32. Wenjun Sun, Siyu Shao, Rui Zhao, Ruqiang Yan, Xingwu Zhang & Xuefeng Chen (2016): A sparse auto-encoder-based deep neural network approach for induction motor faults classification. Measurement 89, pp. 171–178, doi:10.1016/j.measurement.2016.04.007.
  33. Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow & Rob Fergus (2013): Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199, doi:10.48550/arXiv.1312.6199.
  34. Hoang-Dung Tran, Stanley Bak, Weiming Xiang & Taylor T Johnson (2020): Verification of deep convolutional neural networks using imagestars. In: International Conference on Computer Aided Verification. Springer, pp. 18–42, doi:10.1007/978-3-030-53288-8_2.
  35. Hoang-Dung Tran, Patrick Musau, Diego Manzanas Lopez, Xiaodong Yang, Luan Viet Nguyen, Weiming Xiang & Taylor T. Johnson (2019): Parallelizable Reachability Analysis Algorithms for Feed-Forward Neural Networks. In: 7th International Conference on Formal Methods in Software Engineering (FormaliSE2019), Montreal, Canada, doi:10.1109/FormaliSE.2019.00012.
  36. Hoang-Dung Tran, Patrick Musau, Diego Manzanas Lopez, Xiaodong Yang, Luan Viet Nguyen, Weiming Xiang & Taylor T. Johnson (2019): Star-Based Reachability Analysis for Deep Neural Networks. In: 23rd International Symposisum on Formal Methods (FM'19). Springer International Publishing, doi:10.1007/978-3-030-30942-8_39.
  37. Hoang-Dung Tran, Neelanjana Pal, Patrick Musau, Xiaodong Yang, Nathaniel P Hamilton, Diego Manzanas Lopez, Stanley Bak & Taylor T Johnson (2021): Robustness Verification of Semantic Segmentation Neural Networks using Relaxed Reachability. In: Proceedings of the 33rd International Conference on Computer-Aided Verification. Springer, doi:10.1007/978-3-030-81685-8_12.
  38. Hoang-Dung Tran, Xiaodong Yang, Diego Manzanas Lopez, Patrick Musau, Luan Viet Nguyen, Weiming Xiang, Stanley Bak & Taylor T Johnson (2020): NNV: The neural network verification tool for deep neural networks and learning-enabled cyber-physical systems. In: International Conference on Computer Aided Verification. Springer, pp. 3–17, doi:10.1007/978-3-030-53288-8_1.
  39. Pascal Vincent, Hugo Larochelle, Yoshua Bengio & Pierre-Antoine Manzagol (2008): Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th international conference on Machine learning, pp. 1096–1103, doi:10.1145/1390156.1390294.
  40. Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, Pierre-Antoine Manzagol & Léon Bottou (2010): Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion.. Journal of machine learning research 11(12).
  41. Shiqi Wang, Kexin Pei, Justin Whitehouse, Junfeng Yang & Suman Jana (2018): Efficient formal safety analysis of neural networks. In: Advances in Neural Information Processing Systems, pp. 6369–6379, doi:10.48550/arXiv.1809.08098.
  42. Shiqi Wang, Kexin Pei, Justin Whitehouse, Junfeng Yang & Suman Jana (2018): Formal Security Analysis of Neural Networks using Symbolic Intervals. arXiv preprint arXiv:1804.10829, doi:10.48550/arXiv.1804.10829.
  43. Tsui-Wei Weng, Huan Zhang, Hongge Chen, Zhao Song, Cho-Jui Hsieh, Duane Boning, Inderjit S Dhillon & Luca Daniel (2018): Towards Fast Computation of Certified Robustness for ReLU Networks. arXiv preprint arXiv:1804.09699, doi:10.48550/arXiv.1804.09699.
  44. Weiming Xiang, Hoang-Dung Tran & Taylor T Johnson (2017): Reachable set computation and safety verification for neural networks with ReLU activations. arXiv preprint arXiv:1712.08163, doi:10.48550/arXiv.1712.08163.
  45. Weiming Xiang, Hoang-Dung Tran & Taylor T Johnson (2018): Output reachable set estimation and verification for multilayer neural networks. IEEE transactions on neural networks and learning systems 29(11), pp. 5777–5783, doi:10.1109/TNNLS.2018.2808470.
  46. Weiming Xiang, Hoang-Dung Tran & Taylor T Johnson (2019): Specification-Guided Safety Verification for Feedforward Neural Networks. AAAI Spring Symposium on Verification of Neural Networks, doi:10.48550/arXiv.1812.06161.
  47. C. Zhang, W. Gao, J. Song & J. Jiang (2016): An imbalanced data classification algorithm of improved autoencoder neural network. In: 2016 Eighth International Conference on Advanced Computational Intelligence (ICACI), pp. 95–99, doi:10.1109/ICACI.2016.7449810.
  48. Huan Zhang, Tsui-Wei Weng, Pin-Yu Chen, Cho-Jui Hsieh & Luca Daniel (2018): Efficient neural network robustness certification with general activation functions. In: Advances in Neural Information Processing Systems, pp. 4944–4953, doi:10.48550/arXiv.1811.00866.
  49. Chong Zhou & Randy C Paffenroth (2017): Anomaly detection with robust deep autoencoders. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 665–674, doi:10.1145/3097983.3098052.
  50. W. Zhou, J. Berrio, S. Worrall & Eduardo M. Nebot (2020): Automated Evaluation of Semantic Segmentation Robustness for Autonomous Driving. IEEE Transactions on Intelligent Transportation Systems 21, pp. 1951–1963, doi:10.1109/TITS.2019.2909066.

Comments and questions to: eptcs@eptcs.org
For website issues: webmaster@eptcs.org