50 citations · 103 across the 6 of their papers we have counts for
6 papers
Abstraction and Symbolic Execution of Deep Neural Networks with Bayesian Approximation of Hidden Features
Nicolas Berthier, Amany Alshareef, James Sharp +2
Intensive research has been conducted on the verification and validation of deep neural networks (DNNs), aiming to understand if, and how, DNNs can be applied to safety critical ap…
Formal Verification of Robustness and Resilience of Learning-Enabled State Estimation Systems
Wei Huang, Yifan Zhou, Gaojie Jin +4
This paper presents a formal verification guided approach for a principled design and implementation of robust and resilient learning-enabled systems. We focus on learning-enabled…
A Safety Framework for Critical Systems Utilising Deep Neural Networks
Xingyu Zhao, Alec Banks, James Sharp +4
Increasingly sophisticated mathematical modelling processes from Machine Learning are being used to analyse complex data. However, the performance and explainability of these model…
Reliability Validation of Learning Enabled Vehicle Tracking
Youcheng Sun, Yifan Zhou, Simon Maskell +2
This paper studies the reliability of a real-world learning-enabled system, which conducts dynamic vehicle tracking based on a high-resolution wide-area motion imagery input. The s…
Coverage Guided Testing for Recurrent Neural Networks
Wei Huang, Youcheng Sun, Xingyu Zhao +4
Recurrent neural networks (RNNs) have been applied to a broad range of applications, including natural language processing, drug discovery, and video recognition. Their vulnerabili…
testRNN: Coverage-guided Testing on Recurrent Neural Networks
Wei Huang, Youcheng Sun, Xiaowei Huang +1
Recurrent neural networks (RNNs) have been widely applied to various sequential tasks such as text processing, video recognition, and molecular property prediction. We introduce th…