Development of a Neural Network-Based Mathematical Operation Protocol for Embedded Hexadecimal Digits Using Neural Architecture Search (NAS)
arXiv:2211.15416v1h-index: 21
Originality Synthesis-oriented
AI Analysis
This work addresses a domain-specific problem for embedded systems, but it is incremental as it compares existing methods without introducing major innovations.
The paper tackled the problem of efficiently performing addition with embedded hexadecimal digits using machine learning, finding that a human-developed model achieved a final testing loss of 0.2937.
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.