Enhancing Noisy Binary Search Efficiency through Deep Reinforcement Learning-Reference-Cited by-同舟云学术

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Enhancing Noisy Binary Search Efficiency through Deep Reinforcement Learning

Published:2023-08 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI)
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Author:

Ma Rui¹,Tao Yudong¹,Khodeiry Mohamed M.²,Alawa Karam A.²,Shyu Mei-Ling³,Lee Richard K.¹

Affiliation:

1. University of Miami,Department of Electrical and Computer Engineering,Coral Gables,FL,USA

2. University of Miami Miller School of Medicine,Bascom Palmer Eye Institute,Miami,FL,USA

3. University of Missouri-Kansas City,School of Science and Engineering,Kansas City,MO,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10229112/10229323/10229433.pdf?arnumber=10229433

Reference24 articles.

1. Sequential Learning of CSI for MmWave Initial Alignment

2. Incorporating Spatial Models in Visual Field Test Procedures

3. Multiband noisy spectrum sensing with codebooks;ronquillo;Conference Record-Asilomar Conference on Signals Systems and Computers,2017

4. Efficient and unbiased modifications of the QUEST threshold method: Theory, simulations, experimental evaluation and practical implementation

5. A survey on deep learning: Algorithms, techniques, and applications;pouyanfar;ACM Comput Surv,2018

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