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Hi Jax team,
We want to calculate hessians of a likelihood function involving an ode integration so that we can do variational inference. We are running into an issue with `custom_vjp`, which we do…
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Is chumpy using Forward mode or Reverse mode automatic differentiation? A quick browse seems like forward mode...
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Automatic differentiation would be useful so we don't have to implement linearizations or Jacobians of complicated analytic functions. Examples where autodiff would help:
- Jacobians and Hessians o…
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In this issue, we would like to share a draft implementation plan for the forward mode autodiff.
# Background
In general, there are two modes for autodiff: reverse mode and forward mode. The t…
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An implementation of forward-over-reverse, i.e. applying forward mode autodiff to the result of reverse autodiff, can be a way to exploit AD for the computation of Hessians. It might make sense to dir…
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In the notebooks on the documentation here:
https://jax.readthedocs.io/en/latest/notebooks/autodiff_cookbook.html
The author describes topics that he would like to showcase in a future Autodiff co…
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I was wondering is it possible to define both custom vjp and jvp for a function?
```
from jax import custom_vjp, custom_jvp, jacfwd, jacrev
def f(x):
return x
f = custom_jvp(f)
f.d…
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```julia
using Enzyme
function f_ip(x, tmp)
tmp .= x ./ 2
return dot(tmp, x)
end
function f_gradient_deferred!(dx, x, tmp)
dtmp = make_zero(tmp)
autodiff_deferred(Reverse, …
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So far we have only worked on forward mode autodiff. However, to implement backprop and build some simple machine learning application we'll need to implement some reverse mode autodiff.
For now I'…
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I've run into an issue where I want to compute the gradient of an implicit function that itself depends on another implicit function. I can do the operation successfully with `FowardDiff`, however I …