Guy Katz, Clark Barrett, David L Dill, Kyle Julian, and Mykel J Kochenderfer. Reluplex: An efficient smt solver for verifying deep neural networks. In International conference on computer aided verification, pages 97-117. Springer, 2017.
J Kindermann and A Linden. Inversion of neural networks by gradient descent. Parallel Computing, 14(3):277-286, 1990. ISSN 0167-8191. doi: https://doi.org/10.1016/0167-8191(90)90081-J. URL https://www.sciencedirect.com/science/article/pii/016781919090081J.
Shiyu Liang, Yixuan Li, and R. Srikant. Enhancing the reliability of out-of-distribution image detection in neural networks. In International Conference on Learning Representations, 2018. URL https://openreview.net/forum?id=H1VGkIxRZ.
Alexander Meinke, Julian Bitterwolf, and Matthias Hein. Provably robust detection of out-of-distribution data (almost) for free. arXiv preprint arXiv:2106.04260, 2021.
Mark Niklas Müller, Christopher Brix, Stanley Bak, Changliu Liu, and Taylor T. Johnson. The third international verification of neural networks competition (vnn-comp 2022): Summary and results, 2023.
Alessandro De Palma, Rudy Bunel, Krishnamurthy Dvijotham, M. Pawan Kumar, and Robert Stanforth. IbP regularization for verified adversarial robustness via branch-and-bound, 2023.
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. Pytorch: An imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems 32, pages 8024-8035. Curran Associates, Inc., 2019. URL http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf.
Aditi Raghunathan, Jacob Steinhardt, and Percy Liang. Certified defenses against adversarial examples. arXiv preprint arXiv:1801.09344, 2018.
Joseph Redmon and Ali Farhadi. YOLO9000: better, faster, stronger. CoRR, abs/1612.08242, 2016. URL http://arxiv.org/abs/1612.08242.
Nicholas Rober, Michael Everett, Songan Zhang, and Jonathan P How. A hybrid partitioning strategy for backward reachability of neural feedback loops. arXiv preprint arXiv:2210.07918, 2022a.
Nicholas Rober, Sydney M Katz, Chelsea Sidrane, Esen Yel, Michael Everett, Mykel J Kochenderfer, and Jonathan P How. Backward reachability analysis of neural feedback loops: Techniques for linear and nonlinear systems. arXiv preprint arXiv:2209.14076, 2022b.
Emad W. Saad and Donald C. Wunsch. Neural network explanation using inversion. *Neural Networks*, 20(1):78-93, 2007. ISSN 0893-6080. doi: https://doi.org/10.1016/j.neunet.2006.07.005. URL https://www.sciencedirect.com/science/article/pii/S0893608006001730.
Mohammadreza Salehi, Hossein Mirzaei, Dan Hendrycks, Yixuan Li, Mohammad Hossein Rohban, and Mohammad Sabokrou. A unified survey on anomaly, novelty, open-set, and out-of-distribution detection: Solutions and future challenges. Transactions on Machine Learning Research, 2022. URL https://openreview.net/forum?id=aRtjVZvbpK.
Hadi Salman, Greg Yang, Huan Zhang, Cho-Jui Hsieh, and Pengchuan Zhang. A convex relaxation barrier to tight robustness verification of neural networks. Advances in Neural Information Processing Systems, 32, 2019.
Vikash Sehwag, Arjun Nitin Bhagoji, Liwei Song, Chawin Sitawarin, Daniel Cullina, Mung Chiang, and Prateek Mittal. Analyzing the robustness of open-world machine learning. In Proceedings of the 12th ACM Workshop on Artificial Intelligence and Security, AISec'19, page 105-116, New York, NY, USA, 2019. Association for Computing Machinery. ISBN 9781450368339. doi: 10.1145/3338501.3357372. URL https://doi.org/10.1145/3338501.3357372.