jax
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scipy.stats.mode
It would be great if you could add the JAX equivalent of scipy.stats.mode, which is currently unavailable.
A use case may be a ML classification task with ensembles, where multiple models give different predictions, and we are interested in finding the most common one.
As an example, consider predictions to be a two-dimensional DeviceArray of predictions, with
shape = (number of model
Bidirectional RNN
Is there a way to train a bidirectional RNN (like LSTM or GRU) on trax nowadays?
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It would be nice to allow a fixed num_steps in HMC. If the trajectory length is fixed and step size is too small, num_steps will turn out to be quite large. This will be helpful for #1355 and #1353.
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Dear Brax team,
Since Brax is fully differentiable, I thought it'd be possible to use it like DiffTaichi or GradSim for system identification (e.g. determining the mass of an object from a trajectory and known force) but I couldn't find any example for this.
Do you happen to have any demo or tips for this?
From the top of my head I would do something like this:
Let's say the task is to es
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jit and unroll don't seem to be documented in https://jaxopt.github.io/stable/_autosummary/jaxopt.GradientDescent.html#jaxopt-gradientdescent
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This issue is part of our Great Code Cleanup 2022. If you're interested in helping out, take a look at this thread, or come join us on Discord and talk with other contributors!
Type hints are used inconsistently in the
transformersrepo across both TF and PT models, and