8 citations · 31 across the 19 of their papers we have counts for
4 papers · 1 filter
Reverse-Mode Automatic Differentiation of Compiled Programs
Max Aehle, Johannes Blühdorn, Max Sagebaum +1
Tools for algorithmic differentiation (AD) provide accurate derivatives of computer-implemented functions for use in, e. g., optimization and machine learning (ML). However, they o…
Forward-Mode Automatic Differentiation of Compiled Programs
Max Aehle, Johannes Blühdorn, Max Sagebaum +1
Algorithmic differentiation (AD) is a set of techniques that provide partial derivatives of computer-implemented functions. Such a function can be supplied to state-of-the-art AD t…
Toward the End-to-End Optimization of Particle Physics Instruments with Differentiable Programming: a White Paper
Tommaso Dorigo, Andrea Giammanco, Pietro Vischia +33
The full optimization of the design and operation of instruments whose functioning relies on the interaction of radiation with matter is a super-human task, given the large dimensi…
Exploration of Differentiability in a Proton Computed Tomography Simulation Framework
Max Aehle, Johan Alme, Gergely Gábor Barnaföldi +47
Objective. Algorithmic differentiation (AD) can be a useful technique to numerically optimize design and algorithmic parameters by, and quantify uncertainties in, computer simulati…