Kai Xu
Orcid: 0000-0001-8122-1419Affiliations:
- Arizona State University, School of Computing, Informatics, and Decision Systems Engineering, Tempe, AZ, USA
According to our database1,
Kai Xu
authored at least 11 papers
between 2016 and 2020.
Collaborative distances:
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Timeline
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Book In proceedings Article PhD thesis Dataset OtherLinks
Online presence:
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on orcid.org
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Bibliography
2020
Cra: A Generic Compression Ratio Adapter for End-To-End Data-Driven Image Compressive Sensing Reconstruction Frameworks.
Proceedings of the 2020 IEEE International Conference on Acoustics, 2020
Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020
2018
A GPU-Outperforming FPGA Accelerator Architecture for Binary Convolutional Neural Networks.
ACM J. Emerg. Technol. Comput. Syst., 2018
CSVideoNet: A Real-Time End-to-End Learning Framework for High-Frame-Rate Video Compressive Sensing.
Proceedings of the 2018 IEEE Winter Conference on Applications of Computer Vision, 2018
LAPRAN: A Scalable Laplacian Pyramid Reconstructive Adversarial Network for Flexible Compressive Sensing Reconstruction.
Proceedings of the Computer Vision - ECCV 2018, 2018
2017
Data-Driven Sampling Matrix Boolean Optimization for Energy-Efficient Biomedical Signal Acquisition by Compressive Sensing.
IEEE Trans. Biomed. Circuits Syst., 2017
A 7.663-TOPS 8.2-W Energy-efficient FPGA Accelerator for Binary Convolutional Neural Networks.
CoRR, 2017
A data-driven compressive sensing framework tailored for energy-efficient wearable sensing.
Proceedings of the 2017 IEEE International Conference on Acoustics, 2017
A 7.663-TOPS 8.2-W Energy-efficient FPGA Accelerator for Binary Convolutional Neural Networks (Abstract Only).
Proceedings of the 2017 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, 2017
2016
CSVideoNet: A Recurrent Convolutional Neural Network for Compressive Sensing Video Reconstruction.
CoRR, 2016
An energy-efficient compressive sensing framework incorporating online dictionary learning for long-term wireless health monitoring.
Proceedings of the 2016 IEEE International Conference on Acoustics, 2016