Note that our original motivation based on removing discontinuity does not strictly apply to this sampling procedure, which still yields a stochastic gradient due to sampling from the Gumbel distribution.
this approach is conceptually related to greedy relaxations since, here, the soft argmax reparametrization reduces gradient variance which may yield a more informative training signal.