This paper discusses a new AD system that correctly and automatically accepts nested and dynamic use of the AD operators, without any manual intervention. The system is based on a new formulation of AD as highly generalized first-class citizens in a ń-calculus, which is briefly described. Because the ń-calculus is the basis for modern programminglanguage implementation techniques, integration of AD into the ń-calculus allows AD to be integrated into an aggressive compiler. We exhibit a research compiler which does this integration, and uses some novel analysis techniques to accept code involving free dynamic use of nested AD operators, yet performs as well as or better than the most aggressive existing AD systems
Where dual-numbers forward-mode automatic differentiation (AD) pairs each scalar value with its tang...
We describe an implementation of the FARFEL FORTRAN AD extensions (Radul et al., 2012). These exten...
We apply program verification technology to the problem of specifying and verifying automatic differ...
This paper discusses a new AD system that correctly and automatically accepts nested and dynamic use...
Summary. This paper discusses a new AD system that correctly and automatically accepts nested and dy...
This paper discusses a new AD system that correctly and automatically accepts nested and dynamic use...
Current implementations of automatic differentiation are far from automatic. We survey the difficult...
Current implementations of automatic differentiation are far from automatic. We survey the difficult...
This paper discusses a new Automatic Differentiation (AD) system that correctly and automatically ac...
We show that Automatic Differentiation (AD) operators can be provided in a dynamic language without ...
We show that reverse-mode AD (Automatic Differentiation)—a generalized gradient-calculation operato...
Tools for algorithmic differentiation (AD) provide accurate derivatives of computer-implemented func...
We show that reverse-mode AD (Automatic Differentiation)—a generalized gradient-calculation operator...
Where dual-numbers forward-mode automatic differentiation (AD) pairs each scalar value with its tang...
We describe an implementation of the FARFEL FORTRAN AD extensions (Radul et al., 2012). These exten...
We apply program verification technology to the problem of specifying and verifying automatic differ...
This paper discusses a new AD system that correctly and automatically accepts nested and dynamic use...
Summary. This paper discusses a new AD system that correctly and automatically accepts nested and dy...
This paper discusses a new AD system that correctly and automatically accepts nested and dynamic use...
Current implementations of automatic differentiation are far from automatic. We survey the difficult...
Current implementations of automatic differentiation are far from automatic. We survey the difficult...
This paper discusses a new Automatic Differentiation (AD) system that correctly and automatically ac...
We show that Automatic Differentiation (AD) operators can be provided in a dynamic language without ...
We show that reverse-mode AD (Automatic Differentiation)—a generalized gradient-calculation operato...
Tools for algorithmic differentiation (AD) provide accurate derivatives of computer-implemented func...
We show that reverse-mode AD (Automatic Differentiation)—a generalized gradient-calculation operator...
Where dual-numbers forward-mode automatic differentiation (AD) pairs each scalar value with its tang...
We describe an implementation of the FARFEL FORTRAN AD extensions (Radul et al., 2012). These exten...
We apply program verification technology to the problem of specifying and verifying automatic differ...