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JMLR

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A Common Interface for Automatic Differentiation
Guillaume Da · 2026-01-01 · via JMLR

Guillaume Dalle, Adrian Hill; 27(25):1−13, 2026.

Abstract

For scientific machine learning tasks with a lot of custom code, picking the right Automatic Differentiation (AD) system matters. Our Julia package DifferentiationInterface.jl provides a common frontend to a dozen AD backends, unlocking easy comparison and modular development. In particular, its built-in preparation mechanism leverages the strengths of each backend by amortizing one-time computations. This is key to enabling sophisticated features like sparsity handling without putting additional burdens on the user.

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