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pro vyhledávání: '"Sharp, James"'
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 applications. However, existing verification and validation techniques a
Externí odkaz:
http://arxiv.org/abs/2103.03704
Publikováno v:
Eur. Phys. J. E 43, 20 (2020)
Periodic wrinkling of a rigid capping layer on a deformable substrate provides a useful method for templating surface topography for a variety of novel applications. Many experiments have studied wrinkle formation during the compression of a rigid fi
Externí odkaz:
http://arxiv.org/abs/2004.08624
Autor:
Zhao, Xingyu, Banks, Alec, Sharp, James, Robu, Valentin, Flynn, David, Fisher, Michael, Huang, Xiaowei
Increasingly sophisticated mathematical modelling processes from Machine Learning are being used to analyse complex data. However, the performance and explainability of these models within practical critical systems requires a rigorous and continuous
Externí odkaz:
http://arxiv.org/abs/2003.05311
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 system consists of multiple neural network components -- to process the
Externí odkaz:
http://arxiv.org/abs/2002.02424
Akademický článek
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Autor:
Huang, Wei, Sun, Youcheng, Zhao, Xingyu, Sharp, James, Ruan, Wenjie, Meng, Jie, Huang, Xiaowei
Recurrent neural networks (RNNs) have been applied to a broad range of applications, including natural language processing, drug discovery, and video recognition. Their vulnerability to input perturbation is also known. Aligning with a view from soft
Externí odkaz:
http://arxiv.org/abs/1911.01952
Recurrent neural networks (RNNs) have been widely applied to various sequential tasks such as text processing, video recognition, and molecular property prediction. We introduce the first coverage-guided testing tool, coined testRNN, for the verifica
Externí odkaz:
http://arxiv.org/abs/1906.08557
Autor:
Huang, Xiaowei, Kroening, Daniel, Ruan, Wenjie, Sharp, James, Sun, Youcheng, Thamo, Emese, Wu, Min, Yi, Xinping
In the past few years, significant progress has been made on deep neural networks (DNNs) in achieving human-level performance on several long-standing tasks. With the broader deployment of DNNs on various applications, the concerns over their safety
Externí odkaz:
http://arxiv.org/abs/1812.08342
Deep neural networks (DNNs) have a wide range of applications, and software employing them must be thoroughly tested, especially in safety-critical domains. However, traditional software test coverage metrics cannot be applied directly to DNNs. In th
Externí odkaz:
http://arxiv.org/abs/1803.04792