16 citations · 32 across the 9 of their papers we have counts for
5 papers · 1 filter
Convergence for Discrete Parameter Update Schemes
Paul Wilson, Fabio Zanasi, George Constantinides
Modern deep learning models require immense computational resources, motivating research into low-precision training. Quantised training addresses this by representing training com…
Deep Learning with Parametric Lenses
Geoffrey S. H. Cruttwell, Bruno Gavranovic, Neil Ghani +2
We propose a categorical semantics for machine learning algorithms in terms of lenses, parametric maps, and reverse derivative categories. This foundation provides a powerful expla…
Categories of Differentiable Polynomial Circuits for Machine Learning
Paul Wilson, Fabio Zanasi
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 trainin…
Category Theory in Machine Learning
Dan Shiebler, Bruno Gavranović, Paul Wilson
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 languag…
Categorical Foundations of Gradient-Based Learning
G. S. H. Cruttwell, Bruno Gavranović, Neil Ghani +2
We propose a categorical semantics of gradient-based machine learning algorithms in terms of lenses, parametrised maps, and reverse derivative categories. This foundation provides…