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Xiaowei Huang
Xiaowei Huang
Professor of Computer Science, University of Liverpool
在 liverpool.ac.uk 的电子邮件经过验证 - 首页
标题
引用次数
引用次数
年份
Safety verification of deep neural networks
X Huang, M Kwiatkowska, S Wang, M Wu
International Conference on Computer Aided Verification, 3-29, 2017
7582017
Concolic Testing for Deep Neural Networks
Y Sun, M Wu, W Ruan, X Huang, M Kwiatkowska, D Kroening
ASE2018, 2018
2162018
Reachability Analysis of Deep Neural Networks with Provable Guarantees
W Ruan, X Huang, M Kwiatkowska
IJCAI2018, 2018
1962018
Feature-guided black-box safety testing of deep neural networks
M Wicker, X Huang, M Kwiatkowska
International Conference on Tools and Algorithms for the Construction and …, 2018
1862018
Testing deep neural networks
Y Sun, X Huang, D Kroening, J Sharp, M Hill, R Ashmore
arXiv preprint arXiv:1803.04792, 2018
1802018
A survey of safety and trustworthiness of deep neural networks: Verification, testing, adversarial attack and defence, and interpretability
X Huang, D Kroening, W Ruan, J Sharp, Y Sun, E Thamo, M Wu, X Yi
Computer Science Review 37, 100270, 2020
1482020
A game-based approximate verification of deep neural networks with provable guarantees
M Wu, M Wicker, W Ruan, X Huang, M Kwiatkowska
Theoretical Computer Science 807, 298-329, 2020
802020
Structural test coverage criteria for deep neural networks
Y Sun, X Huang, D Kroening, J Sharp, M Hill, R Ashmore
Proceedings of the 41st International Conference on Software Engineering …, 2019
622019
An epistemic strategy logic
X Huang, RVD Meyden
ACM Transactions on Computational Logic (TOCL) 19 (4), 26, 2018
61*2018
Symbolic model checking epistemic strategy logic
X Huang, R van der Meyden
Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence …, 2014
472014
Global Robustness Evaluation of Deep Neural Networks with Provable Guarantees for the Hamming Distance
W Ruan, M Wu, Y Sun, X Huang, D Kroening, M Kwiatkowska
International Joint Conference on Artificial Intelligence, 2019
462019
trustworthiness of deep neural networks: A survey
X Huang, D Kroening, M Kwiatkowska, W Ruan, Y Sun, E Thamo, M Wu, ...
arXiv preprint arXiv:1812.08342, 2018
442018
Analyzing Deep Neural Networks with Symbolic Propagation: Towards Higher Precision and Faster Verification
J Li, J Liu, P Yang, L Chen, X Huang, L Zhang
International Static Analysis Symposium, 296-319, 2019
392019
DeepConcolic: testing and debugging deep neural networks
Y Sun, X Huang, D Kroening, J Sharp, M Hill, R Ashmore
2019 IEEE/ACM 41st International Conference on Software Engineering …, 2019
392019
Reasoning about cognitive trust in stochastic multiagent systems
X Huang, M Kwiatkowska, M Olejnik
ACM Transactions on Computational Logic (TOCL) 20 (4), 1-64, 2019
332019
A safety framework for critical systems utilising deep neural networks
X Zhao, A Banks, J Sharp, V Robu, D Flynn, M Fisher, X Huang
International Conference on Computer Safety, Reliability, and Security, 244-259, 2020
312020
Symbolic model checking of probabilistic knowledge
X Huang, C Luo, R Van Der Meyden
Proceedings of the 13th Conference on Theoretical Aspects of Rationality and …, 2011
292011
Probabilistic alternating-time temporal logic of incomplete information and synchronous perfect recall
X Huang, K Su, C Zhang
Proceedings of the AAAI Conference on Artificial Intelligence 26 (1), 765-771, 2012
282012
A survey of safety and trustworthiness of deep neural networks
X Huang, D Kroening, W Ruan, J Sharp, Y Sun, E Thamo, M Wu, X Yi
arXiv preprint arXiv:1812.08342, 2018
222018
Global Robustness Evaluation of Deep Neural Networks with Provable Guarantees for the Norm
W Ruan, M Wu, Y Sun, X Huang, D Kroening, M Kwiatkowska
arXiv preprint arXiv:1804.05805, 2018
222018
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