Re:Automatic Differentiation with Julia
by gugagore on Tuesday October 23, 2018 @01:37PM
(#18285529)
presumably, source-to-source "AD" could do some symbolic reasoning (whether any systems do, and whether it still counts as AD, I don't actually know). Specifically, I have in mind a source-to-source system that could look at `sqrt(x^2)` and produce a valid sub-gradient at x=0. With AD (whether forward-mode or reverse), you get NaN=Inf*0, because the gradient of x^2 is 0 and the gradient of `sqrt` is Inf.
But yes, dual numbers only do forward mode. However, slightly related is tracker types, like ReverseDiff.jl or Flux.jl's Tracker, where you essentially push a type forward through the code similarly to a dual number, and use this to build the computational graph which you then run backwards with the chain rule.