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I'm trying to differentiate the MJX step function via the autograd function `jax.grad()` in JAX, like:
```
def step(vel, pos):
mjx_data = mjx.make_data(mjx_model)
mjx_data = mjx_data.replace(q…
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Reverse-mode AD implementations calculate a Jacobian row by row instead of column by column. Thus it would be nice to have a way to do row-wise matrix partitioning and coloring. Once we have these col…
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I noticed a couple of issues on Looper behaviour.
1) Trying to play a loop on reverse mode:
- If start is in minimum value, loop is played correctly
- If start is not in minimum value, lo…
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**Author: hikerstk**
Reverse mode should be locked, and instead one more track should be unlocked (since atm there are only three tracks unlocked from the very beginning).
Can easily be postponed ti…
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### LiquidBounce Branch
Nextgen
### Describe your feature request.
There should be a rotation mode for scaffold that will try to get it to rotate in the direction closest to the 90th degree. For ex…
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### 🐛 Describe the bug
When I use forward and reverse AD(automatic differentiation), the calculated gradients are not consistent. I'm wondering what's goning on.
```
from helper_torch import (
…
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Currently there's no way to differentiate variadic functions in reverse, hessian and jacobian mode.
To implement differentiating variadic functions in these modes, we have _atleast_ 3 syntax possib…
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Hi,
I have an optimization problem where my cost function _f_ depends on _n_ (usually >100) input variables (vector **FA**) and also a handful of vector parameters with the same size:
~~~c++
va…
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This probably shouldn't be active all the time, but it would be something to activate when running behind a reverse proxy, in order to set the request protocol / host / port correctly based on X-Forwa…
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When I run test1.ksc, I see that the generated code contains functions named `rev$foo` that compute gradients, however they are not using reverse-mode AD. These generated functions are simply computi…