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Simon See
Researcher at Nvidia
Publications - 136
Citations - 2013
Simon See is an academic researcher from Nvidia. The author has contributed to research in topics: Computer science & Grid. The author has an hindex of 19, co-authored 111 publications receiving 1537 citations. Previous affiliations of Simon See include Singapore University of Technology and Design & Nanyang Technological University.
Papers
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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.
Book ChapterDOI
Learning Common and Specific Features for RGB-D Semantic Segmentation with Deconvolutional Networks
TL;DR: The proposed feature transformation network achieves competitive segmentation accuracy on NYU depth dataset V1 and V2 and takes advantage of deconvolutional networks which can predict pixel-wise class labels, and develops a new structure for deconvolved of multiple modalities.
Proceedings ArticleDOI
Sensor grid: integration of wireless sensor networks and the grid
TL;DR: A sensor grid architecture, called the scalable proxy-based architecture for sensor grid (SPRING), is proposed to address design issues and develop a sensor grid testbed to study the design issues of sensor grids and to improve the design architecture design.
Posted Content
Learning Common and Specific Features for RGB-D Semantic Segmentation with Deconvolutional Networks
TL;DR: Wang et al. as discussed by the authors proposed a feature transformation network to bridge the convolutional networks and deconvolutional network for RGB-D semantic segmentation of indoor images, which achieved competitive segmentation accuracy on NYU depth dataset V1 and V2.