mlsanityData to 14 Oct 2026

A Common Interface for Automatic Differentiation

arXiv 2505.05542 ↗First posted 8 May 2025cs.MS cs.LG cs.NAcited by 0

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 27 references that are in the map. First posted on arXiv 8 May 2025.

Builds on

8 of 27 references.

References · 8

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2015Automatic differentiation in machine learning: a surveycited by 855
  3. 2019A Differentiable Programming System to Bridge Machine Learning and Scientific Computingcited by 83
  4. 2018Don't Unroll Adjoint: Differentiating SSA-Form Programscited by 45
  5. 2020Instead of Rewriting Foreign Code for Machine Learning, Automatically Synthesize Fast Gradientscited by 27
  6. 2024Differentiable Programming for Differential Equations: A Reviewcited by 21
  7. 2021High-performance symbolic-numerics via multiple dispatchcited by 6
  8. 2025Sparser, Better, Faster, Stronger: Sparsity Detection for Efficient Automatic Differentiationcited by 4

Built on by

Not cited yet.