论文标题
机器学习的可区分多项式电路类别
Categories of Differentiable Polynomial Circuits for Machine Learning
论文作者
论文摘要
反向导数类别（RDC）最近被证明是研究机器学习算法的合适语义框架。尽管重点是培训方法，但较少的关注量投入了特定的\ emph {model类}：形态学代表机器学习模型的具体类别。在本文中，我们研究了发电机和RDC类方程的演讲。特别是，我们将\ emph {多项式电路}作为合适的机器学习模型。我们为这些电路提供了公理化，并证明了功能完整性结果。最后，我们讨论了多项式电路在特定半仪上使用离散值进行机器学习。
Reverse derivative categories (RDCs) have recently been shown to be a suitable semantic framework for studying machine learning algorithms. Whereas emphasis has been put on training methodologies, less attention has been devoted to particular \emph{model classes}: the concrete categories whose morphisms represent machine learning models. In this paper we study presentations by generators and equations of classes of RDCs. In particular, we propose \emph{polynomial circuits} as a suitable machine learning model. We give an axiomatisation for these circuits and prove a functional completeness result. Finally, we discuss the use of polynomial circuits over specific semirings to perform machine learning with discrete values.
