Peng Zhang

Orcid: 0000-0003-4355-5949

Affiliations:
  • National University of Defense Technology, National Laboratory for Parallel and Distributed Processing, Changsha, China


According to our database1, Peng Zhang authored at least 11 papers between 2016 and 2022.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

Online presence:

On csauthors.net:

Bibliography

2022
Hybrid Beamforming Based on an Unsupervised Deep Learning Network for Downlink Channels With Imperfect CSI.
IEEE Wirel. Commun. Lett., 2022

A pipelining strategy for accelerating convolution neural networks on ARM CPUs.
Concurr. Comput. Pract. Exp., 2022

2021
A review of artificial intelligence methods combined with Raman spectroscopy to identify the composition of substances.
CoRR, 2021

A high-throughput scalable BNN accelerator with fully pipelined architecture.
CCF Trans. High Perform. Comput., 2021

FEDI: Few-shot learning based on Earth Mover's Distance algorithm combined with deep residual network to identify diabetic retinopathy.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2021

2020
Objectness Consistent Representation for Weakly Supervised Object Detection.
Proceedings of the MM '20: The 28th ACM International Conference on Multimedia, 2020

Noise Reduction Technique for Raman Spectrum using Deep Learning Network.
Proceedings of the 13th International Symposium on Computational Intelligence and Design, 2020

Rethinking Segmentation Guidance for Weakly Supervised Object Detection.
Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

2019
A Pipelining Strategy for Accelerating Convolutional Networks on ARM Processors.
Proceedings of the Parallel Architectures, Algorithms and Programming, 2019

2017
Airport Detection on Optical Satellite Images Using Deep Convolutional Neural Networks.
IEEE Geosci. Remote. Sens. Lett., 2017

2016
Airport detection from remote sensing images using transferable convolutional neural networks.
Proceedings of the 2016 International Joint Conference on Neural Networks, 2016


  Loading...