Structured Bayesian Compression for Deep Neural Networks Based on the Turbo-VBI Approach-Reference-Cited by-同舟云学术

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Structured Bayesian Compression for Deep Neural Networks Based on the Turbo-VBI Approach

Published:2023 Issue: Volume:71 Page:670-685
ISSN:1053-587X
Container-title:IEEE Transactions on Signal Processing
language:
Short-container-title:IEEE Trans. Signal Process.

Author:

Xia Chengyu¹^ORCID,Tsang Danny H. K.²^ORCID,Lau Vincent K. N.¹^ORCID

Affiliation:

1. Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong

2. Internet of Things Thrust, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, China

Funder

Hong Kong Research Grants Council

Guangzhou Municipal Science and Technology Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/78/10040758/10064105.pdf?arnumber=10064105

Reference41 articles.

1. CHEX: CHannel EXploration for CNN Model Compression

2. Convergence of a Block Coordinate Descent Method for Nondifferentiable Minimization

3. The lottery ticket hypothesis: Finding sparse, trainable neural networks;frankle;Proc Int Conf Learn Representations,0

4. Variational dropout and the local reparameterization trick;kingma;Proc Adv Neural Inf Process Syst,0

5. Pruner to Predictor: An Efficient Pruning Method for Neural Networks Compression

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