(a) Draw topic assignment According to the generative process above and the marginalization of t i , the likelihood p(x) of the document x can be derived as where Further, we adopt AEVB and the reparameterization trick to achieve posterior inference and parameter learning.
similar to (Srivastava and Sutton, 2017), we first draw a Gaussian random vector by a reparameterization trick and then pass it through a softmax function to parameterize the multinomial document topic distributions.