Topic models have been widely explored as probabilistic generative models of
documents. Traditional inference methods have sought closed-form 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.

这篇论文介绍了神经方法在主题建模中的应用，特别是提供可参数化分布以允许可变参数下的后向传播，同时提出了一种递归网络，能够类似于贝叶斯非参数主题模型发现数量概念上无限的主题，在 MXM Song Lyrics，20NewsGroups 和 Reuters News 数据集上得到了实验结果的证明。