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
We describe the automatic generation of a provably correct compiler for a non-trivial subset of Ada....
We describe an implementation of the FARFEL FORTRAN AD extensions (Radul et al., 2012). These exten...
We describe the automatic generation of a provably correct com-piler for a non-trivial subset of Ada...
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...
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 ...
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...
International audienceWe apply program verification technology to the problem of specifying and veri...
We show that reverse-mode AD (Automatic Differentiation)—a generalized gradient-calculation operator...
We show that reverse-mode AD (Automatic Differentiation)—a generalized gradient-calculation operato...
1 Abstract. In scientic computing, we often require the derivatives @f=@x of a function f expressed ...
We describe the automatic generation of a provably correct compiler for a non-trivial subset of Ada....
We describe an implementation of the FARFEL FORTRAN AD extensions (Radul et al., 2012). These exten...
We describe the automatic generation of a provably correct com-piler for a non-trivial subset of Ada...
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...
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 ...
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...
International audienceWe apply program verification technology to the problem of specifying and veri...
We show that reverse-mode AD (Automatic Differentiation)—a generalized gradient-calculation operator...
We show that reverse-mode AD (Automatic Differentiation)—a generalized gradient-calculation operato...
1 Abstract. In scientic computing, we often require the derivatives @f=@x of a function f expressed ...
We describe the automatic generation of a provably correct compiler for a non-trivial subset of Ada....
We describe an implementation of the FARFEL FORTRAN AD extensions (Radul et al., 2012). These exten...
We describe the automatic generation of a provably correct com-piler for a non-trivial subset of Ada...