text: abstract
  topic models have been widely explored as probabilistic generative models of documents. traditional inference methods have sought closedform derivations for updating the models, however as the expressiveness of these models grows,
  so does the difficulty of performing fast and
  accurate inference over their parameters. this
  paper presents alternative neural approaches to
  topic modelling by providing parameterisable
  distributions over topics which permit training
  by backpropagation in the framework of neural variational inference. in addition, with the
  help of a stick-breaking construction, we propose a recurrent network that is able to discover a notionally unbounded number of topics, analogous to bayesian non-parametric topic
  models. experimental results on the mxm
  song lyrics, 20newsgroups and reuters news
  datasets demonstrate the effectiveness and efficiency of these neural topic models.