Ranganath Krishnan

@LeopoldACC Quantization of variational parameters in the Bayesian layers is not supported currently.

@pierreosselin can you check how you are getting model output in your code? If return_kl flag is True (by default), the forward function returns a tuple (output and kl) -...

closing the issue due to no response from the user.

@Zepp3 you can calculate the variance or standard deviation with multiple Monte Carlo samples (forward-passes) from the Bayesian neural network model for regression (example snipped below): ``` predictions_mc = []...

@LeopoldACC Quantization of Bayesian deep neural network models are supported in the latest release v0.4.0. Please check out!

> Hi @feracero, I am currently also thinking about using this repo for regression tasks. Did you have success? > > @ranganathkrishnan (and other contributors) would it be possible to...

> Nice, thank you @ranganathkrishnan. > > Is there a specific reason why only LSTMs and not GRUs or RNNs are supported [here](https://github.com/IntelLabs/bayesian-torch/blob/main/bayesian_torch/models/dnn_to_bnn.py#L152)? Or in other words, why did you...

@dashangyanhua Bayesian-Torch can be used to create BNNs utilizing the Bayesian layers from the repo. Here is a simple example to create the model with variational Bayesian layers - https://github.com/IntelLabs/bayesian-torch/blob/main/bayesian_torch/models/bayesian/simple_cnn_variational.py