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I'm interested in implementing autodiff based on expression templates. This would enable compile-time symbolic differentiation that reduces runtime cost. However, this may restrict runtime functionali…
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From @compnerd’s comment in https://github.com/tensorflow/swift-apis/pull/1184
> The project doesn't seem particularly enticing to me - the "rebuilding from the ground up for GPU acceleration thro…
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## Description
There are two ways to improve the way in which we compute the Jacobian matrix of an algebraic solver:
* compute the Jacobian of the system with respect to the parameters and the unkno…
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|------------------|-----------------|
|Previous ID | SR-15849 |
|Radar | None |
|Original Reporter | @BradLarson …
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When I run the following I get an error. I am on Julia 1.8.0, Enzyme 0.10.4 and CUDA 3.12.0.
```julia
using Enzyme, CUDA, StaticArrays
struct Atom{T}
σ::T
ϵ::T
end
function force(c1…
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Expressions containing `pow(x,2)` can produce NaNs when computing a Hessian in reverse mode.
In the example of computing the gradient and Hessian for the Rosenbrock function below, the computed gradi…
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Hello,
How does auto differentiation for a loss function work which itself is an outcome of an iterative process?
In particular, loss(x, a) = min_z f(z, x, a), where the min_z f can be solved wit…
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Is this package for deterministic or stochastic difference equations?
It would be great to fill both left quadrants
![image](https://user-images.githubusercontent.com/7883904/136105161-d1430f4e-0…
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Hi @HajimeKawahara,
Thanks for your excellent work on this toolkit! I'm beginning to experiment with it, and I have a couple of questions that I couldn't answer with the docs or the paper in my fi…
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I'm looking for a reverse mode autodiff package that supports in-place mutation and @ChrisRackauckas suggested this. Unfortunately it crashed on first attempt:
```julia
julia> using FillArrays, Band…