# DiffSharp: An AD Library for .NET Languages

arXiv: https://arxiv.org/abs/1611.03423 · HTML: https://mlsanity.com/paper/1611.03423/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Nov 2016.

## Built on by (most cited first, 3 of 3)

- [Decomposing reverse-mode automatic differentiation](https://mlsanity.com/paper/2105.09469/) (2021)
- [On the Correctness of Automatic Differentiation for Neural Networks with Machine-Representable Parameters](https://mlsanity.com/paper/2301.13370/) (2023)
- [Functorial String Diagrams for Reverse-Mode Automatic Differentiation](https://mlsanity.com/paper/2107.13433/) (2021)

## Abstract (arXiv)

DiffSharp is an algorithmic differentiation or automatic differentiation (AD) library for the .NET ecosystem, which is targeted by the C# and F# languages, among others. The library has been designed with machine learning applications in mind, allowing very succinct implementations of models and optimization routines. DiffSharp is implemented in F# and exposes forward and reverse AD operators as general nestable higher-order functions, usable by any .NET language. It provides high-performance linear algebra primitives---scalars, vectors, and matrices, with a generalization to tensors underway---that are fully supported by all the AD operators, and which use a BLAS/LAPACK backend via the highly optimized OpenBLAS library. DiffSharp currently uses operator overloading, but we are developing a transformation-based version of the library using F#'s "code quotation" metaprogramming facility. Work on a CUDA-based GPU backend is also underway.