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Development of a Neural Network-Based Mathematical Operation Protocol for Embedded Hexadecimal Digits Using Neural Architecture Search (NAS)

Development of a Neural Network-Based Mathematical Operation Protocol for Embedded Hexadecimal Digits Using Neural Architecture Search (NAS)

来源:Arxiv_logoArxiv
英文摘要

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.

Victor Robila、Kexin Pei、Junfeng Yang

Hunter College High SchoolColumbia UniversityColumbia University

计算技术、计算机技术

Victor Robila,Kexin Pei,Junfeng Yang.Development of a Neural Network-Based Mathematical Operation Protocol for Embedded Hexadecimal Digits Using Neural Architecture Search (NAS)[EB/OL].(2022-11-12)[2025-08-23].https://arxiv.org/abs/2211.15416.点此复制

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