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The Ethereal
Development of a Neural Network-Based Mathematical Operation Protocol for Embedded Hexadecimal Digits Using Neural Architecture Search (NAS)
November 12, 2022 ยท Declared Dead ยท ๐ arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Victor Robila, Kexin Pei, Junfeng Yang
arXiv ID
2211.15416
Category
cs.NE: Neural & Evolutionary
Cross-listed
cs.LG
Citations
0
Venue
arXiv.org
Last Checked
4 months ago
Abstract
It is beneficial to develop an efficient machine-learning based method for addition using embedded hexadecimal digits. Through a comparison between human-developed machine learning model and models sampled through Neural Architecture Search (NAS) we determine an efficient approach to solve this problem with a final testing loss of 0.2937 for a human-developed model.
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