Reachability analysis of neural networks using mixed monotonicity

L. Jaulin, M. Kieffer, O. Didrit, and E. Walter, Applied interval analysis: with examples in parameter and state estimation, robust control and robotics . Springer Science & Business Media, 2001

2001

Earlier work this paper cites.

P. Ramachandran, B. Zoph, and Q. V. Le, “Searching for activation functions,” arXiv preprint arXiv:1710.05941 , 2017

Original

2017

Earlier work this paper cites.

W. Xiang, P. Musau, A. A. Wild, D. M. Lopez, N. Hamilton, X. Yang, J. Rosenfeld, and T. T. Johnson, “Verification for machine learning, autonomy, and neural networks survey,” arXiv preprint arXiv:1810.01989 , 2018

Original

2018

Earlier work this paper cites.

M. Akintunde, A. Lomuscio, L. Maganti, and E. Pirovano, “Reachability analysis for neural agent-environment systems,” in Sixteenth International Conference on Principles of Knowledge Representation and Reasoning , 2018

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 , 2018

2018

Earlier work this paper cites.

——, “Efficient formal safety analysis of neural networks,” in 32nd Conference on Neural Information Processing Systems , 2018

2018

Earlier work this paper cites.

H. Zhang, T.-W. Weng, P.-Y. Chen, C.-J. Hsieh, and L. Daniel, “Efficient neural network robustness certification with general activation functions,” Advances in Neural Information Processing Systems , vol. 31, pp. 4939–4948, 2018

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.

A. Raghunathan, J. Steinhardt, and P. Liang, “Certified defenses against adversarial examples,” in International Conference on Learning Representations , 2018

2018

Cited alongside, same era.

H. Salman, G. Yang, H. Zhang, C.-J. Hsieh, and P. Zhang, “A convex relaxation barrier to tight robustness verification of neural networks,” arXiv preprint arXiv:1902.08722 , 2019

Original

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.

D. Misra, “Mish: A self regularized non-monotonic activation function,” arXiv preprint arXiv:1908.08681 , 2019

Original

2019

Cited alongside, same era.

H.-D. Tran, X. Yang, D. M. Lopez, P. Musau, L. V. Nguyen, W. Xiang, S. Bak, and T. T. Johnson, “NNV: The neural network verification tool for deep neural networks and learning-enabled cyber-physical systems,” in International Conference on Computer Aided Verification . Springer, 2020, pp. 3–17

2020

Later among the works it cites.

E. Botoeva, P. Kouvaros, J. Kronqvist, A. Lomuscio, and R. Misener, “Efficient verification of relu-based neural networks via dependency analysis,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 34, no. 04, 2020, pp. 3291–3299

2020

Later among the works it cites.

C. Liu, T. Arnon, C. Lazarus, C. Barrett, and M. J. Kochenderfer, “Algorithms for verifying deep neural networks,” Foundation and Trend in Optimization , vol. 4, no. 3-4, pp. 244–404, 2021

2021

Closest in time.

M. Zhu, W. Min, Q. Wang, S. Zou, and X. Chen, “PFLU and FPFLU: Two novel non-monotonic activation functions in convolutional neural networks,” Neurocomputing , vol. 429, pp. 110–117, 2021