MCMC: Multi-Constrained Model Compression via One-Stage Envelope Reinforcement Learning-Reference-Cited by-同舟云学术

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MCMC: Multi-Constrained Model Compression via One-Stage Envelope Reinforcement Learning

Published:2024 Issue: Volume: Page:1-13
ISSN:2162-237X
Container-title:IEEE Transactions on Neural Networks and Learning Systems
language:
Short-container-title:IEEE Trans. Neural Netw. Learning Syst.

Author:

Li Siqi¹,Chen Jun¹^ORCID,Liu Shanqi¹^ORCID,Zhu Chengrui¹,Tian Guanzhong²^ORCID,Liu Yong³^ORCID

Affiliation:

1. Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, China

2. Ningbo Innovation Center, Zhejiang University, Hangzhou, China

3. State Key Laboratory of Industrial Control Technology and the Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, China

Funder

Major Scientific and Technological Innovation Project of Hangzhou City

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/5962385/6104215/10416765.pdf?arnumber=10416765

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Optimization of a Cluster-Based Energy Management System Using Deep Reinforcement Learning Without Affecting Prosumer Comfort: V2X Technologies and Peer-to-Peer Energy Trading;IEEE Access;2024

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