Many of the current automatic differentiation (AD) tools have similar characteristics. Unfortunately, the similarities between these various AD tools often cannot be easily ascertained by reading the corresponding documentation. To clarify this situation, a taxonomy of AD tools is presented. The taxonomy places AD tools into the Elemental, Extensional, Integral, Operational, and Symbolic classes. This taxonomy is used to classify twenty-nine AD tools. Each tool is examined individually with respect to the mode of differentiation used and the degree of derivatives computed. A list detailing the availability of the surveyed AD tools is provided in the Appendix. 54 refs., 3 figs., 1 tab
International audienceWe present Automatic Differentiation (AD),a technique to obtain derivatives of...
Automatic Differentiation is a relatively recent technique developed for the differentiation of func...
Derivatives, mostly in the form of gradients and Hessians, are ubiquitous in machine learning. Autom...
Abstract. Many of the current automatic dierentiation (AD) tools have similar characteristics. Unfor...
Many of the current automatic differentiation (AD) tools have similar characteristics. Unfortunatel...
The authors give a gentle introduction to using various software tools for Automatic Differentiation...
Full text of this paper is not available in the UHRAThis paper gives an introduction to a number of ...
Automatic differentiation (AD) is a methodology for developing sensitivity-enhanced versions of arbi...
Automatic differentiation (AD) has proven its interest in many fields of applied mathematics, but it...
In mathematics and computer algebra, automatic differentiation (AD) is a set of techniques to evalua...
Many applications require the derivatives of functions defined by computer programs. Automatic diffe...
This thesis is an exposition of the article Arithmetic of Differentiation by L.B Rall. It gives a ...
Automatic Differentiation (AD) is a tool that systematically implements the chain rule of differenti...
summary:Automatic differentiation is an effective method for evaluating derivatives of function, whi...
Automatic Differentiation (AD) automatically performs the differentiation of models expressed in a h...
International audienceWe present Automatic Differentiation (AD),a technique to obtain derivatives of...
Automatic Differentiation is a relatively recent technique developed for the differentiation of func...
Derivatives, mostly in the form of gradients and Hessians, are ubiquitous in machine learning. Autom...
Abstract. Many of the current automatic dierentiation (AD) tools have similar characteristics. Unfor...
Many of the current automatic differentiation (AD) tools have similar characteristics. Unfortunatel...
The authors give a gentle introduction to using various software tools for Automatic Differentiation...
Full text of this paper is not available in the UHRAThis paper gives an introduction to a number of ...
Automatic differentiation (AD) is a methodology for developing sensitivity-enhanced versions of arbi...
Automatic differentiation (AD) has proven its interest in many fields of applied mathematics, but it...
In mathematics and computer algebra, automatic differentiation (AD) is a set of techniques to evalua...
Many applications require the derivatives of functions defined by computer programs. Automatic diffe...
This thesis is an exposition of the article Arithmetic of Differentiation by L.B Rall. It gives a ...
Automatic Differentiation (AD) is a tool that systematically implements the chain rule of differenti...
summary:Automatic differentiation is an effective method for evaluating derivatives of function, whi...
Automatic Differentiation (AD) automatically performs the differentiation of models expressed in a h...
International audienceWe present Automatic Differentiation (AD),a technique to obtain derivatives of...
Automatic Differentiation is a relatively recent technique developed for the differentiation of func...
Derivatives, mostly in the form of gradients and Hessians, are ubiquitous in machine learning. Autom...