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Yiran Zhao
Researcher at University of Illinois at Urbana–Champaign
Publications - 50
Citations - 1674
Yiran Zhao is an academic researcher from University of Illinois at Urbana–Champaign. The author has contributed to research in topics: Deep learning & Artificial neural network. The author has an hindex of 17, co-authored 45 publications receiving 1157 citations. Previous affiliations of Yiran Zhao include Shanghai Jiao Tong University.
Papers
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Proceedings ArticleDOI
DeepSense: A Unified Deep Learning Framework for Time-Series Mobile Sensing Data Processing
TL;DR: DeepSense as discussed by the authors integrates convolutional and recurrent neural networks to exploit local interactions among similar mobile sensors, merge local interactions of different sensory modalities into global interactions, and extract temporal relationships to model signal dynamics.
Proceedings ArticleDOI
DeepIoT: Compressing Deep Neural Network Structures for Sensing Systems with a Compressor-Critic Framework
TL;DR: The proposed DeepIoT presents a unified approach that compresses all commonly used deep learning structures for sensing applications, including fully-connected, convolutional, and recurrent neural networks, as well as their combinations, and obtains a global view of parameter redundancies, which is shown to produce superior compression.
Journal ArticleDOI
Deep Learning for the Internet of Things
Shuochao Yao,Yiran Zhao,Aston Zhang,Shaohan Hu,Huajie Shao,Chao Zhang,Lu Su,Tarek Abdelzaher +7 more
TL;DR: The authors discuss several core challenges in embedded and mobile deep learning, as well as recent solutions demonstrating the feasibility of building IoT applications that are powered by effective, efficient, and reliable deep learning models.
Proceedings ArticleDOI
FastDeepIoT: Towards Understanding and Optimizing Neural Network Execution Time on Mobile and Embedded Devices
TL;DR: This work proposes a novel framework, called FastDeepIoT, that uncovers the non-linear relation between neural network structure and execution time, then exploits that understanding to find network configurations that significantly improve the trade-off between execution time and accuracy on mobile and embedded devices.
Proceedings ArticleDOI
FastDeepIoT: Towards Understanding and Optimizing Neural Network Execution Time on Mobile and Embedded Devices
TL;DR: FastDeepIoT as discussed by the authors proposes a framework that uncovers the non-linear relation between neural network structure and execution time, then exploits that understanding to find network configurations that significantly improve the trade-off between execution time and accuracy on mobile and embedded devices.