A Common Interface for Automatic Differentiation

For scientific machine learning tasks with a lot of custom code, picking the right Automatic Differentiation (AD) system matters. Our Julia packagethis http URLprovides 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.

@article{dalle2025_2505.05542,
  title={ A Common Interface for Automatic Differentiation },
  author={ Guillaume Dalle and Adrian Hill },
  journal={arXiv preprint arXiv:2505.05542},
  year={ 2025 }
}