The marabou framework for verification and analysis of deep neural networks G Katz, DA Huang, D Ibeling, K Julian, C Lazarus, R Lim, P Shah, ... Computer Aided Verification: 31st International Conference, CAV 2019, New …, 2019 | 572 | 2019 |
Parallelization techniques for verifying neural networks H Wu, A Ozdemir, A Zeljić, K Julian, A Irfan, D Gopinath, S Fouladi, G Katz, ... 2020 Formal Methods in Computer Aided Design (FMCAD), 128-137, 2020 | 60 | 2020 |
An SMT-Based Approach for Verifying Binarized Neural Networks G Amir, H Wu, C Barrett, G Katz Tools and Algorithms for the Construction and Analysis of Systems, 203-222, 2021 | 58 | 2021 |
G2SAT: Learning to Generate SAT Formulas J You, H Wu, C Barrett, R Ramanujan, J Leskovec Advances in neural information processing systems, 10553-10564, 2019 | 36 | 2019 |
Efficient neural network analysis with sum-of-infeasibilities H Wu, A Zeljić, G Katz, C Barrett International Conference on Tools and Algorithms for the Construction and …, 2022 | 34 | 2022 |
Global optimization of objective functions represented by ReLU networks CA Strong, H Wu, A Zeljić, KD Julian, G Katz, C Barrett, MJ Kochenderfer Machine Learning, 2021 | 34 | 2021 |
Deepcert: Verification of contextually relevant robustness for neural network image classifiers C Paterson, H Wu, J Grese, R Calinescu, CS Păsăreanu, C Barrett Computer Safety, Reliability, and Security: 40th International Conference …, 2021 | 18 | 2021 |
Improving sat-solving with machine learning H Wu Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science …, 2017 | 18 | 2017 |
Toward certified robustness against real-world distribution shifts H Wu, T Tagomori, A Robey, F Yang, N Matni, G Pappas, H Hassani, ... 2023 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), 537-553, 2023 | 16 | 2023 |
Towards verification of neural networks for small unmanned aircraft collision avoidance A Irfan, KD Julian, H Wu, C Barrett, MJ Kochenderfer, B Meng, J Lopez 2020 AIAA/IEEE 39th Digital Avionics Systems Conference (DASC), 1-10, 2020 | 16 | 2020 |
On reducing over-approximation errors for neural network verification T Zelazny, H Wu, C Barrett, G Katz Proc. 22nd Int. Conf. on Formal Methods in Computer-Aided Design (FMCAD), 17-26, 2022 | 12* | 2022 |
Scalable verification of GNN-based job schedulers H Wu, C Barrett, M Sharif, N Narodytska, G Singh Proceedings of the ACM on Programming Languages 6 (OOPSLA2), 1036-1065, 2022 | 10 | 2022 |
Verix: Towards verified explainability of deep neural networks M Wu, H Wu, C Barrett Advances in neural information processing systems 36, 2024 | 8 | 2024 |
Learning to generate industrial sat instances H Wu, R Ramanujan Proceedings of the International Symposium on Combinatorial Search 10 (1 …, 2019 | 8 | 2019 |
Convex bounds on the softmax function with applications to robustness verification D Wei, H Wu, M Wu, PY Chen, C Barrett, E Farchi International Conference on Artificial Intelligence and Statistics, 6853-6878, 2023 | 5 | 2023 |
Towards Efficient Verification of Quantized Neural Networks P Huang, H Wu, Y Yang, I Daukantas, M Wu, Y Zhang, C Barrett Proceedings of the AAAI Conference on Artificial Intelligence 38 (19), 21152 …, 2024 | 4 | 2024 |
Lemur: Integrating Large Language Models in Automated Program Verification H Wu, C Barrett, N Narodytska arXiv preprint arXiv:2310.04870, 2023 | 4 | 2023 |
Sat solving in the serverless cloud A Ozdemir, H Wu, C Barrett 2021 Formal Methods in Computer Aided Design (FMCAD), 241-245, 2021 | 4 | 2021 |
Policy-specific abstraction predicate selection in neural policy safety verification M Vinzent, M Wu, H Wu, J Hoffmann Proc. 2nd Workshop on Reliable Data-Driven Planning and Scheduling (RDDPS …, 2023 | 1 | 2023 |
Proof-Stitch: Proof Combination for Divide-and-Conquer SAT Solvers. AA Nair, S Chattopadhyay, H Wu, A Ozdemir, CW Barrett FMCAD, 84-88, 2022 | 1 | 2022 |