First of all, thanks for making all of this code available - it's been great to look through!
Im currently spending some time trying to work through the Weight Uncertainty in Neural Networks in order to implement Bayes-by-Backprop. I was struggling to understand the difference between your implementation of `Bayes-by-Bac
Currently, we support stax but it lacks some popular layers such as RNN. Some other candidates include flax, haiku. It might not take much effort to use those libraries in NumPyro.
We might start with some utilities/handlers in numpyro.contrib.flax or `n
Ruby software to create reports about AIX Power environements (HMC, Power servers, VIOSes, NIM servers etc). Next version will allow also manage it. This API is unstable, stable version is expected in September 2020
Modified TensorFlow implementation for training MCMC samplers on Lattice Gauge Theory models from the paper: Generalizing Hamiltonian Monte Carlo with Neural Network
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Hi @JavierAntoran @stratisMarkou,
First of all, thanks for making all of this code available - it's been great to look through!
Im currently spending some time trying to work through the Weight Uncertainty in Neural Networks in order to implement Bayes-by-Backprop. I was struggling to understand the difference between your implementation of `Bayes-by-Bac