Related papers: Category Theory in Machine Learning

Category Theory in Machine Learning

URL: http://arxiv.org/abs/2106.07032v1
Date: Sun, 13 Jun 2021 15:58:13 GMT
Title: Category Theory in Machine Learning
Authors: Dan Shiebler, Bruno Gavranovi\'c, Paul Wilson
Abstract summary: We document the motivations, goals and common themes across applications of category theory in machine learning. We touch on gradient-based learning, probability, and equivariant learning.
Score: 1.6758573326215689
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Over the past two decades machine learning has permeated almost every realm of technology. At the same time, many researchers have begun using category theory as a unifying language, facilitating communication between different scientific disciplines. It is therefore unsurprising that there is a burgeoning interest in applying category theory to machine learning. We aim to document the motivations, goals and common themes across these applications. We touch on gradient-based learning, probability, and equivariant learning.

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