Also keep an eye on: https://github.com/jrevels/ReverseDiffPrototype.jl

(As the name suggests, they are still prototyping it.)


On Sun, Aug 21, 2016 at 11:59 PM, Andrei Zh <[email protected]>
wrote:

> Forward mode AD works faster for functions R^m -> R^n where m << n, e.g.
> when you have a function of 1 input and return tuple or vector of multiple
> outputs.
> Reverse mode AD works faster for functions R^m -> R^n where m >> n, e.g.
> when you have a function of a vector of inputs and return a single output
> (say, typical loss function in machine learning).
>
> As for packages, ReverseDiffSource generates expressions for derivatives,
> but currently supports only simple `for` loops and the author works on
> adding support for `if`s
> <https://github.com/JuliaDiff/ReverseDiffSource.jl/issues/39>. AFAIK,
> ForwardDiff doesn't have any restrictions on a program structure and should
> work with any type of loops, conditions, recursion, etc., but can't be used
> for code generation (e.g. for GPU).
>
> I'm also currently working on Espresso.jl - package for expression
> manipulation and hybrid differentiation
> <https://github.com/dfdx/Espresso.jl#hybrid-differentiation> (AD
> pipeline, symbolic output) where I drop support for any kind of conditions
> and loops in favor of higher flexibility (e.g. differentiation for nested
> function calls which is an hard to achieve
> <https://github.com/JuliaDiff/ReverseDiffSource.jl/issues/38> in
> ReverseDiffSource). The package is in early testing stage yet, but
> differentiation over expressions with numbers and simple vectors seem to
> work fine, and I'm working hard to add derivatives of higher-order tensors.
>
>
> On Sunday, August 21, 2016 at 6:36:48 AM UTC+3, Benjamin Deonovic wrote:
>>
>> What are pros/cons of the various auto differentiation packages available
>> in Julia? Is there a benefit to using ReverseDiffSource vs ForwardDiff?
>> Thank you
>>
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