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Differentiable causal computations via delayed trace (extended version)

Published online by Cambridge University Press:  22 April 2025

David Sprunger  and
Shin-ya Katsumata Open the ORCID record for Shin-ya Katsumata [Opens in a new window]
David Sprunger*
Affiliation:
Indiana State University, Terre Haute, IN, USA
Shin-ya Katsumata
Affiliation:
National Institute of Informatics, Chiyoda, Tokyo, Japan
*
Corresponding author: David Sprunger; Email: david.sprunger@indstate.edu
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Abstract

We investigate causal computations, which take sequences of inputs to sequences of outputs such that the $n$th output depends on the first $n$ inputs only. We model these in category theory via a construction taking a Cartesian category $\mathbb{C}$ to another category $\mathrm{St}(\mathbb{C})$ with a novel trace-like operation called “delayed trace,” which misses yanking and dinaturality axioms of the usual trace. The delayed trace operation provides a feedback mechanism in $\mathrm{St}(\mathbb{C})$ with an implicit guardedness guarantee. When $\mathbb{C}$ is equipped with a Cartesian differential operator, we construct a differential operator for $\mathrm{St}(\mathbb{C})$ using an abstract version of backpropagation through time (BPTT), a technique from machine learning based on unrolling of functions. This obtains a swath of properties for BPTT, including a chain rule and Schwartz theorem. Our differential operator is also able to compute the derivative of a stateful network without requiring the network to be unrolled.

Keywords

Delayed trace operatorsartesian differential categoriesrecurrent neural networksbackpropagation through timesignal flow graphs

Information

Type
Special Issue: Differential Structures
Information
Mathematical Structures in Computer Science , Volume 35 , 2026 , e10
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Copyright
© The Author(s), 2025. Published by Cambridge University Press

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