  By providing a simple and efficient way of computing low-variance gradients
of continuous random variables, the reparameterization trick has become the
technique of choice for training a variety of latent variable models. However,
it is not applicable to a number of important continuous distributions. We
introduce an alternative approach to computing reparameterization gradients
based on implicit differentiation and demonstrate its broader applicability by
applying it to Gamma, Beta, Dirichlet, and von Mises distributions, which
cannot be used with the classic reparameterization trick. Our experiments show
that the proposed approach is faster and more accurate than the existing
gradient estimators for these distributions.
