Scalable Quantitative Verification For Deep Neural Networks
Author:
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9401807/9401950/09402111.pdf?arnumber=9402111
Cited by 22 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. A Zonotopic Dempster-Shafer Approach to the Quantitative Verification of Neural Networks;Lecture Notes in Computer Science;2024-09-11
2. Preprocessing is What You Need: Understanding and Predicting the Complexity of SAT-based Uniform Random Sampling;Proceedings of the 2024 IEEE/ACM 12th International Conference on Formal Methods in Software Engineering (FormaliSE);2024-04-14
3. Exploring the Computational Complexity of SAT Counting and Uniform Sampling with Phase Transitions;Proceedings of the 2024 IEEE/ACM 46th International Conference on Software Engineering: Companion Proceedings;2024-04-14
4. Training for Verification: Increasing Neuron Stability to Scale DNN Verification;Lecture Notes in Computer Science;2024
5. Taming Reachability Analysis of DNN-Controlled Systems via Abstraction-Based Training;Lecture Notes in Computer Science;2023-12-30
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