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researcher

Qianqian Yang

4 papers here

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author3
  • last author1

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.IT1
  • cs.LG1
  • eess.AS1
  • eess.IV1
same name
  • Qianqian Yang — 11 papers, h 14
  • Qianqian Yang — 7 papers
  • Qianqian Yang — 6 papers, h 21
  • Qianqian Yang — 4 papers
  • Qianqian Yang — 2 papers
  • Qianqian Yang — 2 papers, h 16

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

most citedCommunication-Efficient Federated Learning with Binary Neural Networks

51 citations · 60 across the 4 of their papers we have counts for

collaborators

4 papers

cs.IT2022

Resource Allocation for Capacity Optimization in Joint Source-Channel Coding Systems

Kaiyi Chi, Qianqian Yang, Zhaohui Yang +2

Benefited from the advances of deep learning (DL) techniques, deep joint source-channel coding (JSCC) has shown its great potential to improve the performance of wireless transmiss…

eess.IV2022★ 3 cited

Generative Model Based Highly Efficient Semantic Communication Approach for Image Transmission

Tianxiao Han, Jiancheng Tang, Qianqian Yang +3

Deep learning (DL) based semantic communication methods have been explored to transmit images efficiently in recent years. In this paper, we propose a generative model based semant…

eess.AS2022★ 6 cited

Semantic-preserved Communication System for Highly Efficient Speech Transmission

Tianxiao Han, Qianqian Yang, Zhiguo Shi +2

Deep learning (DL) based semantic communication methods have been explored for the efficient transmission of images, text, and speech in recent years. In contrast to traditional wi…

cs.LG2021★ 51 cited

Communication-Efficient Federated Learning with Binary Neural Networks

Yuzhi Yang, Zhaoyang Zhang, Qianqian Yang

Federated learning (FL) is a privacy-preserving machine learning setting that enables many devices to jointly train a shared global model without the need to reveal their data to a…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.