Computing derivatives using automatic differentiation methods entails a variety of combinatorial problems. The OpenAD tool implements automatic differentiation as source transformation of a program that represents a numerical model. We select three combinatorial problems and discuss the solutions implemented in OpenAD. Our intention is to explain the specific parts of the implementation so that readers can easily use OpenAD to investigate and develop their own solutions to these problems
Derivatives, mostly in the form of gradients and Hessians, are ubiquitous in machine learning. Autom...
This thesis describes the characteristics of a novel computational technique called automatic differ...
Differentiation is one of the fundamental problems in numerical mathemetics. The solution of many op...
Computing derivatives using automatic differentiation methods entails a variety of combinatorial pro...
In mathematics and computer algebra, automatic differentiation (AD) is a set of techniques to evalua...
Full text of this paper is not available in the UHRAThis paper gives an introduction to a number of ...
International audienceThis paper presents our work toward correct and efficient automatic differenti...
Automatic differentiation --- the mechanical transformation of numeric computer programs to calculat...
Automatic differentiation (AD) has proven its interest in many fields of applied mathematics, but it...
Derivatives, mostly in the form of gradients and Hessians, are ubiquitous in machine learning. Autom...
This dissertation is concerned with algorithmic differentiation (AD), which is a method for algorith...
Derivatives, mostly in the form of gradients and Hessians, are ubiquitous in machine learning. Autom...
The context of this work is Automatic Differentiation (AD). Fundamentally, AD transforms a program t...
In this paper we introduce DiffSharp, an automatic differentiation (AD) library designed with machin...
The OpenAD/F tool allows the evaluation of derivatives of functions defined by a Fortran pro-gram. T...
Derivatives, mostly in the form of gradients and Hessians, are ubiquitous in machine learning. Autom...
This thesis describes the characteristics of a novel computational technique called automatic differ...
Differentiation is one of the fundamental problems in numerical mathemetics. The solution of many op...
Computing derivatives using automatic differentiation methods entails a variety of combinatorial pro...
In mathematics and computer algebra, automatic differentiation (AD) is a set of techniques to evalua...
Full text of this paper is not available in the UHRAThis paper gives an introduction to a number of ...
International audienceThis paper presents our work toward correct and efficient automatic differenti...
Automatic differentiation --- the mechanical transformation of numeric computer programs to calculat...
Automatic differentiation (AD) has proven its interest in many fields of applied mathematics, but it...
Derivatives, mostly in the form of gradients and Hessians, are ubiquitous in machine learning. Autom...
This dissertation is concerned with algorithmic differentiation (AD), which is a method for algorith...
Derivatives, mostly in the form of gradients and Hessians, are ubiquitous in machine learning. Autom...
The context of this work is Automatic Differentiation (AD). Fundamentally, AD transforms a program t...
In this paper we introduce DiffSharp, an automatic differentiation (AD) library designed with machin...
The OpenAD/F tool allows the evaluation of derivatives of functions defined by a Fortran pro-gram. T...
Derivatives, mostly in the form of gradients and Hessians, are ubiquitous in machine learning. Autom...
This thesis describes the characteristics of a novel computational technique called automatic differ...
Differentiation is one of the fundamental problems in numerical mathemetics. The solution of many op...