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Found 9 papers, 1 papers with code
Deep Learning with Parametric Lenses
We propose a categorical semantics for machine learning algorithms in terms of lenses, parametric maps, and reverse derivative categories.
Categories of Differentiable Polynomial Circuits for Machine Learning
Reverse derivative categories (RDCs) have recently been shown to be a suitable semantic framework for studying machine learning algorithms.
Functorial String Diagrams for Reverse-Mode Automatic Differentiation
We enhance the calculus of string diagrams for monoidal categories with hierarchical features in order to capture closed monoidal (and cartesian closed) structure.
Categorical Foundations of Gradient-Based Learning
We propose a categorical semantics of gradient-based machine learning algorithms in terms of lenses, parametrised maps, and reverse derivative categories.
Reverse Derivative Ascent: A Categorical Approach to Learning Boolean Circuits
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.
Coalgebraic Semantics for Probabilistic Logic Programming
Probabilistic logic programming is increasingly important in artificial intelligence and related fields as a formalism to reason about uncertainty.
Logic in Computer Science
String Diagram Rewrite Theory I: Rewriting with Frobenius Structure
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
Causal Inference by String Diagram Surgery
We represent the effect of such an intervention as an endofunctor which performs `string diagram surgery' within the syntactic category of string diagrams.
The Logical Essentials of Bayesian Reasoning
This chapter offers an accessible introduction to the channel-based approach to Bayesian probability theory.
