Fetching the paper…
Reading the bibliography…
We consider the problem of certifying the robustness of deep neural networks against real-world distribution shifts.
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE , vol. 86, no. 11, pp. 2278–2324, 1998
1998
Earlier work this paper cites.
V. Vapnik, The nature of statistical learning theory . Springer science & business media, 1999
1999
Earlier work this paper cites.
E. Clarke, O. Grumberg, S. Jha, Y. Lu, and H. Veith, “Counterexample-guided abstraction refinement,” in International Conference on Computer Aided Verification . Springer, 2000, pp. 154–169
2000
Earlier work this paper cites.
A. Krizhevsky, G. Hinton et al. , “Learning multiple layers of features from tiny images,” 2009
2009
Earlier work this paper cites.
B. Biggio, I. Corona, D. Maiorca, B. Nelson, N. Šrndić, P. Laskov, G. Giacinto, and F. Roli, “Evasion attacks against machine learning at test time,” in Joint European conference on machine learning and knowledge discovery in databases . Springer, 2013, pp. 387–402
2013
Earlier work this paper cites.
2013
Earlier work this paper cites.
2014
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” Advances in neural information processing systems , vol. 27, 2014
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
K. Sohn, H. Lee, and X. Yan, “Learning structured output representation using deep conditional generative models,” Advances in neural information processing systems , vol. 28, 2015
2015
Earlier work this paper cites.
S.-M. Moosavi-Dezfooli, A. Fawzi, and P. Frossard, “Deepfool: a simple and accurate method to fool deep neural networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 2574–2582
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778
2016
Earlier work this paper cites.
2017
Earlier work this paper cites.
G. Katz, C. Barrett, D. Dill, K. Julian, and M. Kochenderfer, “Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks,” in Proc. 29th Int. Conf. on Computer Aided Verification (CAV) , 2017, pp. 97–117
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
X. Huang, M. Kwiatkowska, S. Wang, and M. Wu, “Safety verification of deep neural networks,” in CAV , 2017
2017
Earlier work this paper cites.
E. Wong and Z. Kolter, “Provable defenses against adversarial examples via the convex outer adversarial polytope,” in International Conference on Machine Learning . PMLR, 2018, pp. 5286–5295
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
K. Dvijotham, R. Stanforth, S. Gowal, T. A. Mann, and P. Kohli, “A dual approach to scalable verification of deep networks.” in UAI , vol. 1, no. 2, 2018, p. 3
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
S. Wang, K. Pei, J. Whitehouse, J. Yang, and S. Jana, “Formal security analysis of neural networks using symbolic intervals,” in 27th USENIX Security Symposium, USENIX Security 2018, Baltimore, MD, USA, August 15-17, 2018 , 2018, pp. 1599–1614. [Online]. Available: https://www.usenix.org/conference/usenixsecurity18/presentation/wang-shiqi
2018
Earlier work this paper cites.
——, “Efficient formal safety analysis of neural networks,” in Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, 3-8 December 2018, Montréal, Canada , 2018, pp. 6369–6379. [Online]. Available: http://papers.nips.cc/paper/7873-efficient-formal-safety-analysis-of-neural-networks
2018
Earlier work this paper cites.
S. Dutta, S. Jha, S. Sankaranarayanan, and A. Tiwari, “Output range analysis for deep feedforward neural networks,” in NASA Formal Methods - 10th International Symposium, NFM 2018, Newport News, VA, USA, April 17-19, 2018, Proceedings , 2018
2018
Earlier work this paper cites.
L. Weng, H. Zhang, H. Chen, Z. Song, C.-J. Hsieh, L. Daniel, D. Boning, and I. Dhillon, “Towards fast computation of certified robustness for relu networks,” in International Conference on Machine Learning . PMLR, 2018, pp. 5276–5285
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
W. Xiang, H.-D. Tran, and T. T. Johnson, “Output reachable set estimation and verification for multilayer neural networks,” IEEE transactions on neural networks and learning systems , vol. 29, no. 11, pp. 5777–5783, 2018
2018
Cited alongside, same era.
T. Gehr, M. Mirman, D. Drachsler-Cohen, P. Tsankov, S. Chaudhuri, and M. T. Vechev, “AI2: safety and robustness certification of neural networks with abstract interpretation,” in 2018 IEEE Symposium on Security and Privacy, SP 2018, Proceedings, 21-23 May 2018, San Francisco, California, USA , 2018, pp. 3–18. [Online]. Available: https://doi.org/10.1109/SP.2018.00058
2018
Cited alongside, same era.
G. Singh, T. Gehr, M. Mirman, M. Püschel, and M. Vechev, “Fast and effective robustness certification,” Advances in Neural Information Processing Systems , vol. 31, pp. 10 802–10 813, 2018
2018
Cited alongside, same era.
X. Huang, M.-Y. Liu, S. Belongie, and J. Kautz, “Multimodal unsupervised image-to-image translation,” in Proceedings of the European conference on computer vision (ECCV) , 2018, pp. 172–189
R. Bunel, A. De Palma, A. Desmaison, K. Dvijotham, P. Kohli, P. Torr, and M. P. Kumar, “Lagrangian decomposition for neural network verification,” in Conference on Uncertainty in Artificial Intelligence . PMLR, 2020, pp. 370–379
2020
Later among the works it cites.
K. D. Dvijotham, R. Stanforth, S. Gowal, C. Qin, S. De, and P. Kohli, “Efficient neural network verification with exactness characterization,” in Uncertainty in Artificial Intelligence . PMLR, 2020, pp. 497–507
2020
Later among the works it cites.
H.-D. Tran, S. Bak, W. Xiang, and T. T. Johnson, “Verification of deep convolutional neural networks using imagestars,” in International Conference on Computer Aided Verification . Springer, 2020, pp. 18–42
2020
Later among the works it cites.
Z. Lyu, C.-Y. Ko, Z. Kong, N. Wong, D. Lin, and L. Daniel, “Fastened crown: Tightened neural network robustness certificates,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 34, no. 04, 2020, pp. 5037–5044
2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2018
Cited alongside, same era.
A. Cimatti, A. Griggio, A. Irfan, M. Roveri, and R. Sebastiani, “Incremental linearization for satisfiability and verification modulo nonlinear arithmetic and transcendental functions,” ACM Transactions on Computational Logic (TOCL) , vol. 19, no. 3, pp. 1–52, 2018
2018
Cited alongside, same era.
H. Zhang, Y. Yu, J. Jiao, E. Xing, L. El Ghaoui, and M. Jordan, “Theoretically principled trade-off between robustness and accuracy,” in International conference on machine learning . PMLR, 2019, pp. 7472–7482
2019
Cited alongside, same era.
2019
Cited alongside, same era.
G. Katz, D. A. Huang, D. Ibeling, K. Julian, C. Lazarus, R. Lim, P. Shah, S. Thakoor, H. Wu, A. Zeljić et al. , “The marabou framework for verification and analysis of deep neural networks,” in International Conference on Computer Aided Verification , 2019, pp. 443–452
2019
Cited alongside, same era.
G. Anderson, S. Pailoor, I. Dillig, and S. Chaudhuri, “Optimization and abstraction: A synergistic approach for analyzing neural network robustness,” in Proc. Programming Language Design and Implementation (PLDI) , 2019, p. 731–744
2019
Cited alongside, same era.
G. Singh, T. Gehr, M. Püschel, and M. Vechev, “An abstract domain for certifying neural networks,” Proceedings of the ACM on Programming Languages , vol. 3, no. POPL, pp. 1–30, 2019
2019
Cited alongside, same era.
G. Singh, R. Ganvir, M. Püschel, and M. Vechev, “Beyond the single neuron convex barrier for neural network certification,” Advances in Neural Information Processing Systems , vol. 32, pp. 15 098–15 109, 2019
2019
Cited alongside, same era.
2019
Cited alongside, same era.
Later among the works it cites.
2020
Later among the works it cites.
Y. Y. Elboher, J. Gottschlich, and G. Katz, “An abstraction-based framework for neural network verification,” in International Conference on Computer Aided Verification . Springer, 2020, pp. 43–65
2020
Later among the works it cites.
P. Henriksen and A. Lomuscio, “Efficient neural network verification via adaptive refinement and adversarial search,” in ECAI 2020 . IOS Press, 2020, pp. 2513–2520
2020
Later among the works it cites.
S. Gowal, C. Qin, P.-S. Huang, T. Cemgil, K. Dvijotham, T. Mann, and P. Kohli, “Achieving robustness in the wild via adversarial mixing with disentangled representations,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 1211–1220
2020
Later among the works it cites.
K. Xu, Z. Shi, H. Zhang, Y. Wang, K.-W. Chang, M. Huang, B. Kailkhura, X. Lin, and C.-J. Hsieh, “Automatic perturbation analysis for scalable certified robustness and beyond,” Advances in Neural Information Processing Systems , vol. 33, pp. 1129–1141, 2020
2020
Later among the works it cites.
“International verification of neural networks competition (vnn-comp),” 2020, https://sites.google.com/view/vnn20/vnncomp
2020
Later among the works it cites.
A. Robey, L. Chamon, G. J. Pappas, H. Hassani, and A. Ribeiro, “Adversarial robustness with semi-infinite constrained learning,” Advances in Neural Information Processing Systems , vol. 34, pp. 6198–6215, 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
W. Ryou, J. Chen, M. Balunovic, G. Singh, A. Dan, and M. Vechev, “Scalable polyhedral verification of recurrent neural networks,” in International Conference on Computer Aided Verification . Springer, 2021, pp. 225–248
2021
Later among the works it cites.
2021
Later among the works it cites.
S. M. Katz, A. L. Corso, C. A. Strong, and M. J. Kochenderfer, “Verification of image-based neural network controllers using generative models,” in 2021 IEEE/AIAA 40th Digital Avionics Systems Conference (DASC) . IEEE, 2021, pp. 1–10
2021
Later among the works it cites.
M. Mirman, A. Hägele, P. Bielik, T. Gehr, and M. Vechev, “Robustness certification with generative models,” in Proceedings of the 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation , 2021, pp. 1141–1154
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
M. Mann, A. Irfan, A. Griggio, O. Padon, and C. Barrett, “Counterexample-guided prophecy for model checking modulo the theory of arrays,” in International Conference on Tools and Algorithms for the Construction and Analysis of Systems . Springer, 2021, pp. 113–132
2021
Later among the works it cites.
H. Wu, A. Zeljić, G. Katz, and C. Barrett, “Efficient neural network analysis with sum-of-infeasibilities,” in International Conference on Tools and Algorithms for the Construction and Analysis of Systems . Springer, 2022, pp. 143–163
2022
Closest in time.
2022
Closest in time.
M. N. Müller, G. Makarchuk, G. Singh, M. Püschel, and M. Vechev, “Prima: general and precise neural network certification via scalable convex hull approximations,” Proceedings of the ACM on Programming Languages , vol. 6, no. POPL, pp. 1–33, 2022
2022
Closest in time.
2022
Closest in time.
X. Xie, K. Kersting, and D. Neider, “Neuro-symbolic verification of deep neural networks,” 2022
2022
Closest in time.
C. Sidrane, A. Maleki, A. Irfan, and M. J. Kochenderfer, “Overt: An algorithm for safety verification of neural network control policies for nonlinear systems,” Journal of Machine Learning Research , vol. 23, no. 117, pp. 1–45, 2022
2022
Closest in time.