Denoising Noisy Neural Networks: A Bayesian Approach With Compensation-Reference-Cited by-同舟云学术

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Denoising Noisy Neural Networks: A Bayesian Approach With Compensation

Published:2023 Issue: Volume:71 Page:2460-2474
ISSN:1053-587X
Container-title:IEEE Transactions on Signal Processing
language:
Short-container-title:IEEE Trans. Signal Process.

Author:

Shao Yulin¹^ORCID,Liew Soung Chang²^ORCID,Gündüz Deniz³^ORCID

Affiliation:

1. State Key Laboratory of Internet of Things for Smart City, University of Macau, Macau, China

2. Department of Information Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong

3. Department of Electrical and Electronic Engineering, Imperial College London, London, U.K.

Funder

U.K. Research and Innovation

AIR

CUHK Direct

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/78/10040758/10167840.pdf?arnumber=10167840

Reference49 articles.

1. Broadband Analog Aggregation for Low-Latency Federated Edge Learning

2. Federated Learning Over Wireless Fading Channels

3. AirNet: Neural Network Transmission over the Air

4. Federated Learning via Over-the-Air Computation

5. Federated mmWave Beam Selection Utilizing LIDAR Data

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1. AirNet: Neural Network Transmission Over the Air;IEEE Transactions on Wireless Communications;2024-09

2. Multi-class classification of pathological myopia based on fundus photography;Journal of Innovative Optical Health Sciences;2024-08-17

3. Optical Integrated Sensing and Communication with Light-Emitting Diode;2024 IEEE International Conference on Communications Workshops (ICC Workshops);2024-06-09

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