32 citations · 60 across the 4 of their papers we have counts for
14 papers
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…
Functorial String Diagrams for Reverse-Mode Automatic Differentiation
Mario Alvarez-Picallo, Dan R. Ghica, David Sprunger +1
We enhance the calculus of string diagrams for monoidal categories with hierarchical features in order to capture closed monoidal (and cartesian closed) structure. Using this new s…
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…
Reverse Derivative Ascent: A Categorical Approach to Learning Boolean Circuits
Paul Wilson, Fabio Zanasi
We introduce Reverse Derivative Ascent: a categorical analogue of gradient based methods for machine learning. Our algorithm is defined at the level of so-called reverse differenti…
A String Diagrammatic Axiomatisation of Finite-State Automata
Robin Piedeleu, Fabio Zanasi
We develop a fully diagrammatic approach to the theory of finite-state automata, based on reinterpreting their usual state-transition graphical representation as a two-dimensional…
Contextual Equivalence for Signal Flow Graphs
Filippo Bonchi, Robin Piedeleu, Pawel Sobocinski +1
We extend the signal flow calculus---a compositional account of the classical signal flow graph model of computation---to encompass affine behaviour, and furnish it with a novel op…