Work

Efficient differentiable programming in a functional array-processing language

Year: 2019

Type: article

Abstract: We present a system for the automatic differentiation (AD) of a higher-order functional array-processing language. The core functional language underlying this system simultaneously supports both source-to-source forward-mode AD and global optimisations such as loop transformations. In combination, gradient computation with forward-mode AD can be as efficient as reverse mode, and that the Jacobian matrices required for numerical algorithms such as Gauss-Newton and Levenberg-Marquardt can be efficiently computed.

Source: Proceedings of the ACM on Programming Languages

DOI: 10.1145/3341701

Authors: Amir Shaikhha, Andrew Fitzgibbon, Dimitrios Vytiniotis, Simon Peyton Jones

Institutions: University of Oxford, Microsoft Research (United Kingdom), Google DeepMind (United Kingdom)

Language: English

Topic: Logic, programming, and type systems

Subfield: Software

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funder: École Polytechnique Fédérale de Lausanne

OpenAlex ID: W2806441765

FWCI: 9.267

Cites: 78

Cited by: 57

Related to: 10