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Sometimes you actually do want the jacobian of a function. If this function is a vector->vector function, the Jacobian is a matrix. It's not immediately obvious how to define the jacobian for, say, a …
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@MTCam @cmikida2,
Do we need this https://github.com/ecisneros8/pyrometheus/blob/168ad3d360d7a3911a81454c898ad6d9fc813271/pyrometheus/__init__.py#L480
I found that it is messing up Jacobians com…
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Forward sensitivity speeds are currently held back by the parameter jacobian evaluations, which can be enormously expensive to compute for our problems (lots of parameters) without sparse differentiat…
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Current implementation uses a few intermediate matrix multiplication that can be removed and the final results just hardcoded using indexing or basic torch operations.
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We have the infrastructure to create "real" Jacobians from linear maps, using the constructors LMOne, LMZero, LMHCat etc. We don't actually demonstrate and benchmark these on a real Jacobian task, e…
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In PR #70, I added a test that calls the `jacobian` method of a `tf.GradientTape` on the result of a TFQ calculation. The test passes if I do not use the `@tf.function` decorator. However, when I ad…
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Hello @inducer,
What is a good Python package for automatic differentiation? I'd like to start exploring this as an option for Jacobians, as in the original Prometheus.
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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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### Question
Hi, I'm setting up a robot bin picking simulation with top-down camera that captures semantic or instance segmentation after each grasp attempt. My simulation always get `CUDA error: a…
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There are use cases e.g. in optimal control where the trajectories and associated gradients and jacobians are required to be calculated in parallel. An simple example would be generating optimal traj…