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Creators/Authors contains: "Wang, Kaiyuan"

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  1. ; ; ; ; ; (, Neural Networks)
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  2. ; ; (, The Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering)
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  3. ; ; ; (, 31st IEEE International Symposium on Software Reliability Engineering (ISSRE 2020))
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  4. ; ; ; ; ; (, International Journal on Software Tools for Technology Transfer)
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  5. ; ; ; ; ; (, 41st ACM SIGPLAN International Conference on Programming Language Design and Implementation (PLDI))
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  6. ; ; ; ; ; (, 26th International Conference on ools and Algorithms for the Construction and Analysis of Systems (TACAS))
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  7. ; ; ; ; ; (, IEEE 30th International Symposium on Software Reliability Engineering (ISSRE))
    Deep Neural Networks (DNN) are increasingly used in a variety of applications, many of them with serious safety and security concerns. This paper describes DeepCheck, a new approach for validating DNNs based on core ideas from program analysis, specifically from symbolic execution. DeepCheck implements novel techniques for lightweight symbolic analysis of DNNs and applies them to address two challenging problems in DNN analysis: 1) identification of important input features and 2) leveraging those features to create adversarial inputs. Experimental results with an MNIST image classification network and a sentiment network for textual data show that DeepCheck promises to be a valuable tool for DNN analysis. 
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  8. ; ; ; (, 12th IEEE Conference on Software Testing, Validation and Verification (ICST))
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  9. ; ; ; (, 25th International Conference on Tools and Algorithms for the Construction and Analysis of Systems)
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  10. ; ; ; ; (, ACM SIGSOFT Software Engineering Notes)
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