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2016

DiffSharp: An AD Library for .NET Languages

Baydin, Atılım Güneş, Pearlmutter, Barak A., Siskind, Jeffrey Mark

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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: An AD Library for .NET Languages · Around

Built on

Reverse accumulation and attractive fixed points
Bruce Christianson · 1994
Earlier work this paper cites.
Differentiating fixed point iterations with ADOL-C
Sebastian Schlenkirch and Andrea Walther · 2005
Earlier work this paper cites.
Method for computing all occurrences of a compound event from occurrences of primitive events, September6 2005
Jeffrey Mark Siskind · 2005
Earlier work this paper cites.

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Then

Diffsharp: Automatic differentiation library
Original
Atılım Güneş Baydin, Barak A. Pearlmutter, and Jeffrey Mark Siskind · 2015
Later among the works it cites.
GPU-accelerated adjoint algorithmic differentiation
Felix Gremse, Andreas Höfter, Lukas Razik, Fabian Kiessling, and Uwe Naumann · 2016
Closest in time.

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