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1. `auto_jacvec!` and `num_jacvec!` only work when the output and input dimensions are the same, i.e., the jacobian is square. The issue is this line (and the equivalent for `num_jacvec!`), which shou…
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May I know whether the auto differentiation feature working now? The ceres dependency seems to indicate that. But I did some simple test, it seems that numerical jacobian is still the default behavior…
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hi all,
I just bumped into hugot project by coincidence today, when looking for a library to load HF models in Go.
I maintain [GoMLX - github.com/gomlx/gomlx](github.com/gomlx/gomlx) a machine l…
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Currently the custom VJP implementation is restricting the code to reverse mode differentiation. Though this is by far the more common of the two modes, we should add support for forward mode auto-dif…
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The following function gets differentiated incorrectly, as there's no support for the differentiation of comparison operators in for-loop conditions.
```C++
double fn(double u, double v) {
do…
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This issue makes part of #20 more concrete.
Recurrent Neural Networks, became an effective Neural Network architecture, that we would like to implement in Leaf as well. The operations could probably …
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I understand that there is auto-differentiation in the implementation. Can I get the derivative of the xc over the density matrix?
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The current implementation makes use of [autograd](https://github.com/HIPS/autograd/) to auto-differentiate the loss function. As the automatic differentiation is too slow, we chose to not use this fe…
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It would be awesome if the backing array implementation supported auto differentiation, that we could access some `grad` method from Cubed.
It looks like a bunch of stakeholder libraries have this…
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* [Link](https://arxiv.org/abs/1907.13422)
* Title: Use and implementation of autodifferentiation in tensor network methods with complex scalars
* Keywords (optional): auto differentiation, tensor…