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I thank reviewer 3 for raising this issue (rephrased by AHL):
The normalised curvature matrix [used to find the optimal value of alpha] is constructed numerically from chi2 using a forward Euler me…
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Thanks you for sharing this library.
In some case using forward automatic differentiation can be useful, for example when solving non-linear least square problems, where having the jacobian matrix o…
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Hello,
If you get a chance, could you please clarify which numerical differentiation method was used for Figure 3 for the comparison between numerical and automatic differentiation? Is it the cente…
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Perhaps chatGPT can help a lot.
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After talking with @bartgol at the EESM meeting last week, he suggested making a tracking issue to discuss changes that would be useful or necessary for differentiable modeling, specifically with auto…
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It might be nice to add support for [ImplicitDifferentiation.jl](https://github.com/JuliaDecisionFocusedLearning/ImplicitDifferentiation.jl) or similar to make the reconstructed image differentiable w…
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It's interesting to consider if [Enzyme](https://enzyme.mit.edu/) can be integrated into Chapel. It supports automatic differentiation and that's useful for machine learning.
This issue serves as a…
mppf updated
4 months ago
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| Metadata | |
| -------- | --- |
| Owner(s) | @ZuseZ4 |
| Team(s) | [compiler](http://github.com/rust-lang/compiler-team), [lang](http://github.com/rust-lang/lang-team) |
| Goal…
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**Description of bug**
Weird behavior of automatic differentiation: error is thrown for a function, but not for a slightly rewritten (though analogous) function or for a bit more complicated one.
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Should this be implemented using an AD object? Can this AD object handle linear algebra? How much of it is orthogonal to numeric?