4 code implementations • 20 Feb 2015 • Atilim Gunes Baydin, Barak A. Pearlmutter, Alexey Andreyevich Radul, Jeffrey Mark Siskind
Derivatives, mostly in the form of gradients and Hessians, are ubiquitous in machine learning.
no code implementations • 20 Nov 2012 • Oleksandr Manzyuk, Barak A. Pearlmutter, Alexey Andreyevich Radul, David R. Rush, Jeffrey Mark Siskind
The essence of Forward AD is to attach perturbations to each number, and propagate these through the computation.
Symbolic Computation Mathematical Software Differential Geometry