This article provides an overview of some of the mathematical prin- ciples of Automatic Differentiation (AD). In particular, we summarise different descriptions of the Forward Mode of AD, like the matrix-vector product based approach, the idea of lifting functions to the algebra of dual numbers, the method of Taylor series expansion on dual numbers and the application of the push-forward operator, and explain why they all reduce to the same actual chain of computations. We further give a short mathematical description of some methods of higher-order Forward AD and, at the end of this paper, brie y describe the Reverse Mode of Automatic Differentiation
Automatic differentiation—the mechanical transformation of numeric computer programs to calculate de...
Original article can be found at http://www.sciencedirect.com/science/journal/03770427 Copyright Els...
Automatic Differentiation (AD) is a tool that systematically implements the chain rule of differenti...
This article provides an overview of some of the mathematical prin- ciples of Automatic Differentia...
This article provides a short overview of the theory of First Order Automatic Differentiation (AD) f...
Automatic differentiation --- the mechanical transformation of numeric computer programs to calculat...
In mathematics and computer algebra, automatic differentiation (AD) is a set of techniques to evalua...
This paper collects together a number of matrix derivative results which are very useful in forward ...
We study the high order reverse mode of Automatic Differentiation (AD) in the dissertation. Automati...
Automatic differentiation (AD) is conventionally understood as a family of distinct algorithms, root...
summary:Automatic differentiation is an effective method for evaluating derivatives of function, whi...
This thesis is an exposition of the article Arithmetic of Differentiation by L.B Rall. It gives a ...
Derivatives, mostly in the form of gradients and Hessians, are ubiquitous in machine learning. Autom...
Derivatives, mostly in the form of gradients and Hessians, are ubiquitous in machine learning. Autom...
We present semantic correctness proofs of automatic differentiation (AD). Weconsider a forward-mode ...
Automatic differentiation—the mechanical transformation of numeric computer programs to calculate de...
Original article can be found at http://www.sciencedirect.com/science/journal/03770427 Copyright Els...
Automatic Differentiation (AD) is a tool that systematically implements the chain rule of differenti...
This article provides an overview of some of the mathematical prin- ciples of Automatic Differentia...
This article provides a short overview of the theory of First Order Automatic Differentiation (AD) f...
Automatic differentiation --- the mechanical transformation of numeric computer programs to calculat...
In mathematics and computer algebra, automatic differentiation (AD) is a set of techniques to evalua...
This paper collects together a number of matrix derivative results which are very useful in forward ...
We study the high order reverse mode of Automatic Differentiation (AD) in the dissertation. Automati...
Automatic differentiation (AD) is conventionally understood as a family of distinct algorithms, root...
summary:Automatic differentiation is an effective method for evaluating derivatives of function, whi...
This thesis is an exposition of the article Arithmetic of Differentiation by L.B Rall. It gives a ...
Derivatives, mostly in the form of gradients and Hessians, are ubiquitous in machine learning. Autom...
Derivatives, mostly in the form of gradients and Hessians, are ubiquitous in machine learning. Autom...
We present semantic correctness proofs of automatic differentiation (AD). Weconsider a forward-mode ...
Automatic differentiation—the mechanical transformation of numeric computer programs to calculate de...
Original article can be found at http://www.sciencedirect.com/science/journal/03770427 Copyright Els...
Automatic Differentiation (AD) is a tool that systematically implements the chain rule of differenti...