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Found 8 papers, 1 papers with code
Fundamental Components of Deep Learning: A category-theoretic approach
Like the early stages of many scientific disciplines, it is marked by the discovery of new phenomena, ad-hoc design decisions, and the lack of a uniform and compositional mathematical foundation.
Categorical Deep Learning: An Algebraic Theory of Architectures
We present our position on the elusive quest for a general-purpose framework for specifying and studying deep learning architectures.
Graph Convolutional Neural Networks as Parametric CoKleisli morphisms
We define the bicategory of Graph Convolutional Neural Networks $\mathbf{GCNN}_n$ for an arbitrary graph with $n$ nodes.
Space-time tradeoffs of lenses and optics via higher category theory
We establish a conjecture that the well-known isomorphism between cartesian lenses and optics arises out of the lax 2-adjunction between their double-categorical counterparts.
Category Theory in Machine Learning
Over the past two decades machine learning has permeated almost every realm of technology.
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.
Learning Functors using Gradient Descent
Neural networks are a general framework for differentiable optimization which includes many other machine learning approaches as special cases.
Compositional Deep Learning
Neural networks have become an increasingly popular tool for solving many real-world problems.
