  We investigate the use of alternative divergences to Kullback-Leibler (KL) in
variational inference(VI), based on the Variational Dropout \cite{kingma2015}.
Stochastic gradient variational Bayes (SGVB) \cite{aevb} is a general framework
for estimating the evidence lower bound (ELBO) in Variational Bayes. In this
work, we extend the SGVB estimator with using Alpha-Divergences, which are
alternative to divergences to VI' KL objective. The Gaussian dropout can be
seen as a local reparametrization trick of the SGVB objective. We extend the
Variational Dropout to use alpha divergences for variational inference. Our
results compare $\alpha$-divergence variational dropout with standard
variational dropout with correlated and uncorrelated weight noise. We show that
the $\alpha$-divergence with $\alpha \rightarrow 1$ (or KL divergence) is still
a good measure for use in variational inference, in spite of the efficient use
of Alpha-divergences for Dropout VI \cite{Li17}. $\alpha \rightarrow 1$ can
yield the lowest training error, and optimizes a good lower bound for the
evidence lower bound (ELBO) among all values of the parameter $\alpha \in
[0,\infty)$.
