no code implementations • 30 Mar 2024 • Geoffrey S. H. Cruttwell, Bruno Gavranovic, Neil Ghani, Paul Wilson, Fabio Zanasi
We propose a categorical semantics for machine learning algorithms in terms of lenses, parametric maps, and reverse derivative categories.
no code implementations • 12 Mar 2022 • Paul Wilson, Fabio Zanasi
Reverse derivative categories (RDCs) have recently been shown to be a suitable semantic framework for studying machine learning algorithms.
no code implementations • 28 Jul 2021 • Mario Alvarez-Picallo, Dan R. Ghica, David Sprunger, Fabio Zanasi
We enhance the calculus of string diagrams for monoidal categories with hierarchical features in order to capture closed monoidal (and cartesian closed) structure.
no code implementations • 2 Mar 2021 • G. S. H. Cruttwell, Bruno Gavranović, Neil Ghani, Paul Wilson, Fabio Zanasi
We propose a categorical semantics of gradient-based machine learning algorithms in terms of lenses, parametrised maps, and reverse derivative categories.
no code implementations • 26 Jan 2021 • Paul Wilson, Fabio Zanasi
Our motivating example is boolean circuits: we show how our algorithm can be applied to such circuits by using the theory of reverse differential categories.
no code implementations • 7 Dec 2020 • Tao Gu, Fabio Zanasi
Probabilistic logic programming is increasingly important in artificial intelligence and related fields as a formalism to reason about uncertainty.
Logic in Computer Science
1 code implementation • 3 Dec 2020 • Filippo Bonchi, Fabio Gadducci, Aleks Kissinger, Pawel Sobocinski, Fabio Zanasi
In the last part, we also see that the approach can be generalised to model rewriting modulo multiple Frobenius structures.
Logic in Computer Science Category Theory
no code implementations • 20 Nov 2018 • Bart Jacobs, Aleks Kissinger, Fabio Zanasi
We represent the effect of such an intervention as an endofunctor which performs `string diagram surgery' within the syntactic category of string diagrams.
no code implementations • 3 Apr 2018 • Bart Jacobs, Fabio Zanasi
This chapter offers an accessible introduction to the channel-based approach to Bayesian probability theory.