基于残留物类的整数的分类通过现代深度学习算法
Da Wu1, Jingye Yang1, Mian Umair Ahsan2
1Department of Mathematics, University of Pennsylvania, Philadelphia, PA 19104, USA.
Patterns (New York, N.Y.)
|December 18, 2023
概括
特征工程对于机器学习模型来说至关重要,它们可以通过质数余数对整数进行分类. 即使是先进的AutoML和大型语言模型 (LLM) 在没有精心设计的功能的情况下也会扎.
科学领域:
- 数学理论 数学理论
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 通过质数余数对整数进行分类是一个基本的计算任务.
- 深度学习和自动机器学习 (AutoML) 方法越来越多地用于复杂的分类问题.
- 机器学习模型的性能往往取决于输入功能的质量.
研究的目的:
- 调查各种深度学习架构的有效性,并研究基于其余数模小素数的整数分类的特征工程技术.
- 评估领先的AutoML平台在这个特定的数理论任务上的表现.
- 评估大型语言模型 (LLM) 处理此类分类问题的能力,并引入一种新有效的方法.
主要方法:
- 测试各种深度学习架构和特征工程策略.
- 评估来自亚马逊,谷歌和微软的AutoML平台.
- 开发和应用一种使用线性回归在里埃数列基础向量的新方法.
- 评估大型语言模型 (LLM) 的性能,包括GPT-4,GPT-J,LLaMA和Falcon.
主要成果:
- 分类性能高度依赖于所选择的特征空间.
- 没有专家的功能工程,AutoML平台无法执行任务.
- 在里埃数列基础向量上提出的线性回归方法被证明是有效的.
- 大型语言模型 (LLM) 在这个分类任务中表现出了显著的局限性.
结论:
- 功能工程仍然是提高机器学习模型性能和可解释性的关键组成部分.
- 尽管在AutoML和LLMs方面取得了进展,但针对特定,复杂的任务而定制的功能工程至关重要.
- 这项研究强调了机器学习基本原则与尖端技术的持续重要性.
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