r/MachineLearning - The Case for Bayesian Deep Learning

I'm speaking as someone who is very, VERY much into Bayesian and in particular, variational modelling, but dear god can they be finicky. Some of it is just growing pains; I wrote my first VAE in plain Keras, and I remember the pains of having to reparameterize by hand and implementing my own Kullback-Leibler. Nowadays, even plain Torch and Tensorflow come with tools for that. Plus, the theory behind what you're doing is a little bit daunting, since it's less mainstream. Forgot to square the variance param?