6 research outputs found
Confusion of Tagged Perturbations in Forward Automatic Differentiation of Higher-Order Functions
Forward Automatic Differentiation (AD) is a technique for augmenting programs
to both perform their original calculation and also compute its directional derivative. The essence of Forward AD is to attach a derivative value to each number,
and propagate these through the computation. When derivatives are nested, the
distinct derivative calculations, and their associated attached values, must be distinguished. In dynamic languages this is typically accomplished by creating a unique
tag for each application of the derivative operator, tagging the attached values, and
overloading the arithmetic operators. We exhibit a subtle bug, present in fielded
implementations, in which perturbations are confused despite the tagging machinery
Confusion of Tagged Perturbations in Forward Automatic Differentiation of Higher-Order Functions
Forward Automatic Differentiation (AD) is a technique for augmenting programs
to both perform their original calculation and also compute its directional derivative. The essence of Forward AD is to attach a derivative value to each number,
and propagate these through the computation. When derivatives are nested, the
distinct derivative calculations, and their associated attached values, must be distinguished. In dynamic languages this is typically accomplished by creating a unique
tag for each application of the derivative operator, tagging the attached values, and
overloading the arithmetic operators. We exhibit a subtle bug, present in fielded
implementations, in which perturbations are confused despite the tagging machinery
Confusion of Tagged Perturbations in Forward Automatic Differentiation of Higher-Order Functions
Forward Automatic Differentiation (AD) is a technique for augmenting programs
to both perform their original calculation and also compute its directional derivative. The essence of Forward AD is to attach a derivative value to each number,
and propagate these through the computation. When derivatives are nested, the
distinct derivative calculations, and their associated attached values, must be distinguished. In dynamic languages this is typically accomplished by creating a unique
tag for each application of the derivative operator, tagging the attached values, and
overloading the arithmetic operators. We exhibit a subtle bug, present in fielded
implementations, in which perturbations are confused despite the tagging machinery
Confusion of Tagged Perturbations in Forward Automatic Differentiation of Higher-Order Functions
Forward Automatic Differentiation (AD) is a technique for augmenting programs
to both perform their original calculation and also compute its directional derivative. The essence of Forward AD is to attach a derivative value to each number,
and propagate these through the computation. When derivatives are nested, the
distinct derivative calculations, and their associated attached values, must be distinguished. In dynamic languages this is typically accomplished by creating a unique
tag for each application of the derivative operator, tagging the attached values, and
overloading the arithmetic operators. We exhibit a subtle bug, present in fielded
implementations, in which perturbations are confused despite the tagging machinery