深度学习和数据分析中的部分等价度的拓模型
Lucia Ferrari1, Patrizio Frosini1, Nicola Quercioli2
1Department of Mathematics, University of Bologna, Bologna, Italy.
Frontiers in artificial intelligence
|January 8, 2024
概括
我们介绍了使用P-GENEO运算符在神经网络中部分等价度的拓模型. 这些运营商确保数据转换遵守某些对称性,提供近似性和凸度特性,以提高网络性能.
科学领域:
- * 数学 是一个学科.
- * 计算机科学 计算机科学
- * 机器学习 * 机器学习
背景情况:
- *神经网络通常需要数据转换,尊重基本对称性,以提高性能和概括性.
- *编码部分等价值,其中变换不一定是组,在网络设计中提出了重大挑战.
研究的目的:
- * 提出一种新的拓模型来编码神经网络中的部分等价性.
- * 引入和分析用于数据转换的新类运算符,P-GENEO.
- * 调查测量空间和P-GENEOs的属性.
主要方法:
- * 开发一个拓框架来建模部分等差.
- * 引入P-GENEO (部分集通用等效网络运营商) 作为数据转换运营商.
- *测量空间和P-GENEO的数学分析,包括伪度量定义.
主要成果:
- *P-GENEOs被定义为尊重特定集的转换的非扩展性运算符.
- * GENEO (通用等价网络运营商) 是一个特殊的情况,当转换形成一个集团时.
- *这项研究表明,测量结果的空间和P-GENEO具有方便的近似和凸度特性.
结论:
- * 拟议的拓模型有效地编码神经网络中的部分等价值.
- * P-GENEO提供了一种灵活的工具,用于处理具有部分对称性的数据转换.
- *鉴定到的近似性和凸度性质对于这些网络的理论理解和实际应用是有益的.
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