Stable Training of Normalizing Flows for High-dimensional Variational
  Inference
Daniel Andrade
Variational inference with normalizing flows (NFs) is an increasingly popular
alternative to MCMC methods. In particular, NFs based on coupling layers (Real
NVPs) are frequently used due to their good empirical performance. In theory,
increasing the depth of normalizing flows should lead to more accurate
posterior approximations. However, in practice, training deep normalizing flows
for approximating high-dimensional posterior distributions is often infeasible
due to the high variance of the stochastic gradients. In this work, we show
that previous methods for stabilizing the variance of stochastic gradient
descent can be insufficient to achieve stable training of Real NVPs. As the
source of the problem, we identify that, during training, samples often exhibit
unusual high values. As a remedy, we propose a combination of two methods: (1)
soft-thresholding of the scale in Real NVPs, and (2) a bijective soft log
transformation of the samples. We evaluate these and other previously proposed
modification on several challenging target distributions, including a
high-dimensional horseshoe logistic regression model. Our experiments show that
with our modifications, stable training of Real NVPs for posteriors with
several thousand dimensions is possible, allowing for more accurate marginal
likelihood estimation via importance sampling. Moreover, we evaluate several
common training techniques and architecture choices and provide practical
advise for training NFs for high-dimensional variational inference.