  Like all sub-fields of machine learning Bayesian Deep Learning is driven by
empirical validation of its theoretical proposals. Given the many aspects of an
experiment it is always possible that minor or even major experimental flaws
can slip by both authors and reviewers. One of the most popular experiments
used to evaluate approximate inference techniques is the regression experiment
on UCI datasets. However, in this experiment, models which have been trained to
convergence have often been compared with baselines trained only for a fixed
number of iterations. We find that a well-established baseline, Monte Carlo
dropout, when evaluated under the same experimental settings shows significant
improvements. In fact, the baseline outperforms or performs competitively with
methods that claimed to be superior to the very same baseline method when they
were introduced. Hence, by exposing this flaw in experimental procedure, we
highlight the importance of using identical experimental setups to evaluate,
compare, and benchmark methods in Bayesian Deep Learning.
