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First of all, great library! I'm finding it very useful for some projects that I am working on.
However, in some instances I am running into a TypeError in models where an array is being sliced or ass…
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## Description
A couple times we've talked about how expensive all the runtime checks are. In the past we mentioned using some eigen internals to make sure checks only happen once.
Instead, what…
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Hi,
I'm about to start a project that requires automatic differentiation; I have a small set of specific requirements (e.g. I don't need any high level Machine learning functionality) and am trying…
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#### Summary:
We don't actually care about forward mode autodiff with doubles under the hood; we only use the forward mode system for higher order autodiff (and reverse mode `var`s under the hood). W…
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Thank your for sharing the examples.
I followed the instructions to build mitsuba2 with the gpu_autodiff_rgb backend. My system is Ubuntu 20.04 with a Titan V GPU.
I'm attaching the results, whi…
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When I use autodiff, an error message appears:
[verify.cpp:taichi::lang::IRVerifier::basic_verify@46] IR broken: stmt 8233 cannot have operand 8226.
when I close advanced_optimization, the error b…
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For fitting parameter values for ODEs a la the adjoint sensitivity method, we might want to override the gradient computation for the forward ODE solve. More concretely, we might have an integrator fu…
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I think I might have made a mistake in my understanding of `autodiff`. Taking the reverse single variable example as a starting point:
```
var f(var x)
{
return 1 + x + x*x + 1/x + log(x);
…
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I've been doing some test renders with the cbox.xml example scene with different spectral variables, to see which is the fastest; however, I expected GPU rendering to be much faster. Do these times lo…
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JAX supports custom primitives and vjps, just like Autograd did. Improvements:
1) add this to documentation
2) add a minimal example of this in the examples section
3) add a wrapper function if app…