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Tue 10 Nov 2020 01:37 - 01:38 at Virtual room 1 - ML Testing 1

Program slicing has been widely applied in a variety of software engineering tasks. However, existing program slicing techniques only deal with traditional programs that are constructed with instructions and variables, rather than neural networks that are composed of neurons and synapses.
In this paper, we introduce NNSlicer, the first approach for slicing deep neural networks based on data-flow analysis. Our method understands the reaction of each neuron to an input based on the difference between its behavior activated by the input and the average behavior over the whole dataset. Then we quantify the neuron contributions to the slicing criterion by recursively backtracking from the output neurons, and calculate the slice as the neurons and the synapses with larger contributions.
We demonstrate the usefulness and effectiveness of NNSlicer with three applications, including adversarial input detection, model pruning, and selective model protection. In all applications,
NNSlicer significantly outperforms other baselines that do not rely on data flow analysis.

Tue 10 Nov
Times are displayed in time zone: (UTC) Coordinated Universal Time change

01:30 - 02:00
01:30
2m
Talk
Correlations between Deep Neural Network Model Coverage Criteria and Model Quality
Research Papers
Shenao YanRutgers University, USA, Guanhong TaoPurdue University, USA, Xuwei LiuPurdue University, USA, Juan ZhaiRutgers University, USA, Shiqing MaRutgers University, USA, Lei XuNanjing University, China, Xiangyu ZhangPurdue University
DOI
01:33
1m
Talk
Deep Learning Library Testing via Effective Model GenerationACM SIGSOFT Distinguished Paper Award
Research Papers
Zan WangTianjin University, China, Ming YanTianjin University, China, Junjie ChenTianjin University, China, Shuang LiuTianjin University, China, Dongdi ZhangTianjin University, China
DOI
01:35
1m
Talk
Detecting Numerical Bugs in Neural Network ArchitecturesACM SIGSOFT Distinguished Paper Award
Research Papers
Yuhao ZhangPeking University, Luyao RenPeking University, China, Liqian ChenNational University of Defense Technology, China, Yingfei XiongPeking University, Shing-Chi CheungHong Kong University of Science and Technology, China, Tao XiePeking University
DOI
01:37
1m
Talk
Dynamic Slicing for Deep Neural Networks
Research Papers
Ziqi ZhangPeking University, China, Yuanchun LiMicrosoft Research, China, Yao GuoPeking University, Xiangqun ChenPeking University, Yunxin LiuMicrosoft Research, China
DOI
01:39
1m
Talk
Grammar Based Directed Testing of Machine Learning Systems
Journal First
Sakshi UdeshiSingapore University of Technology and Design, Sudipta ChattopadhyaySingapore University of Technology and Design
01:41
1m
Talk
Is Neuron Coverage a Meaningful Measure for Testing Deep Neural Networks?
Research Papers
Fabrice Harel-CanadaUniversity of California at Los Angeles, USA, Lingxiao WangUniversity of California at Los Angeles, USA, Muhammad Ali GulzarUniversity of California at Los Angeles, USA, Quanquan GuUniversity of California at Los Angeles, USA, Miryung KimUniversity of California at Los Angeles, USA
DOI
01:43
1m
Talk
Operational Calibration: Debugging Confidence Errors for DNNs in the Field
Research Papers
Zenan LiNanjing University, China, Xiaoxing MaNanjing University, China, Chang XuNanjing University, China, Jingwei XuNanjing University, China, Chun CaoNanjing University, China, Jian LuNanjing University, China
DOI
01:45
15m
Talk
Conversations on ML Testing 1
Research Papers
Fabrice Harel-CanadaUniversity of California at Los Angeles, USA, Ming YanTianjin University, China, Sakshi UdeshiSingapore University of Technology and Design, Shenao YanRutgers University, USA, Yuhao ZhangPeking University, Zenan LiNanjing University, China, Ziqi ZhangPeking University, China, M: Hamid BagheriUniversity of Nebraska-Lincoln, USA