Year: 2025
Type: conference-paper
Abstract: Automatic differentiation (AD) is a family of algorithms with many applications in scientific computing and machine learning. They compute numerical derivatives by interpreting or transforming the source code of numeric expressions. The correctness and efficiency of such AD algorithms has been the subject of much research, in particular that of reverse-mode AD which is particularly efficient for computing large gradients, but the code it generates is highly non-obvious.
Authors: Samuel Klumpers, Tom Schrijvers
Institution: KU Leuven
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Funder: Fonds Wetenschappelijk Onderzoek
OpenAlex ID: W4414990079