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Felix Juefei-Xu
Researcher at Alibaba Group
Publications - 117
Citations - 4618
Felix Juefei-Xu is an academic researcher from Alibaba Group. The author has contributed to research in topics: Computer science & Facial recognition system. The author has an hindex of 28, co-authored 106 publications receiving 3181 citations. Previous affiliations of Felix Juefei-Xu include Cleveland State University & Carnegie Mellon University.
Papers
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Proceedings ArticleDOI
DeepGauge: multi-granularity testing criteria for deep learning systems
Lei Ma,Felix Juefei-Xu,Fuyuan Zhang,Jiyuan Sun,Minhui Xue,Bo Li,Chunyang Chen,Ting Su,Li Li,Yang Liu,Jianjun Zhao,Yadong Wang +11 more
TL;DR: DeepGauge is proposed, a set of multi-granularity testing criteria for DL systems, which aims at rendering a multi-faceted portrayal of the testbed and sheds light on the construction of more generic and robust DL systems.
Proceedings ArticleDOI
DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems
Lei Ma,Felix Juefei-Xu,Fuyuan Zhang,Jiyuan Sun,Minhui Xue,Bo Li,Chunyang Chen,Ting Su,Li Li,Yang Liu,Jianjun Zhao,Yadong Wang +11 more
TL;DR: DeepGauge as discussed by the authors proposes a set of multi-granularity testing criteria for DL systems, which aims at rendering a multi-faceted portrayal of the testbed.
Proceedings ArticleDOI
DeepHunter: a coverage-guided fuzz testing framework for deep neural networks
Xiaofei Xie,Lei Ma,Felix Juefei-Xu,Minhui Xue,Hongxu Chen,Yang Liu,Jianjun Zhao,Bo Li,Jianxiong Yin,Simon See +9 more
TL;DR: DeepHunter, a coverage-guided fuzz testing framework for detecting potential defects of general-purpose DNNs, is proposed and a metamorphic mutation strategy to generate new semantically preserved tests is proposed, and multiple extensible coverage criteria as feedback to guide the test generation.
Proceedings ArticleDOI
DeepMutation: Mutation Testing of Deep Learning Systems
Lei Ma,Lei Ma,Fuyuan Zhang,Jiyuan Sun,Minhui Xue,Bo Li,Felix Juefei-Xu,Chao Xie,Li Li,Yang Liu,Jianjun Zhao,Yadong Wang +11 more
TL;DR: This paper proposes a mutation testing framework specialized for DL systems to measure the quality of test data, and designs a set of model-level mutation operators that directly inject faults into DL models without a training process.
Proceedings ArticleDOI
Local Binary Convolutional Neural Networks
TL;DR: Local Binary Convolutional Neural Networks (LBCNNs) as mentioned in this paper uses a set of fixed sparse pre-defined binary convolutional filters that are not updated during the training process.