论文标题
使用神经架构搜索（NAS）开发基于神经网络的数学操作协议，用于嵌入式十六进制数字
Development of a Neural Network-Based Mathematical Operation Protocol for Embedded Hexadecimal Digits Using Neural Architecture Search (NAS)
论文作者
论文摘要
开发一种有效的基于机器学习方法的方法是有益的，用于使用嵌入式十六进制数字进行添加。通过对人类开发的机器学习模型和通过神经结构搜索（NAS）进行采样的模型之间的比较，我们确定了一种有效的方法来解决此问题，最终的测试损失为人类开发的模型为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.
