Autor:	Bersier, Stephane, Chen-Lin, Xinyi
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence Computer Science - Programming Languages Computer Science - Software Engineering
Druh dokumentu:	Working Paper
Popis:	Despite the wide variety of input types in machine learning, this diversity is often not fully reflected in their representations or model architectures, leading to inefficiencies throughout a model's lifecycle. This paper introduces an algebraic approach to constructing input-encoding architectures that properly account for the data's structure, providing a step toward achieving more typeful machine learning. Comment: 25 pages, 6 figures. Keywords: typeful, algebraic data types, tensors, structured data
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2410.11776 Zobrazit plný text záznamu View this record from Arxiv