A Common Interface for Automatic Differentiation
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908367485665280 |
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| author | Dalle, Guillaume Hill, Adrian |
| author_facet | Dalle, Guillaume Hill, Adrian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_05542 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Common Interface for Automatic Differentiation Dalle, Guillaume Hill, Adrian Mathematical Software Machine Learning Numerical Analysis G.1.4 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. |
| title | A Common Interface for Automatic Differentiation |
| topic | Mathematical Software Machine Learning Numerical Analysis G.1.4 |
| url | https://arxiv.org/abs/2505.05542 |