  Visualization and topic modeling are widely used approaches for text
analysis. Traditional visualization methods find low-dimensional
representations of documents in the visualization space (typically 2D or 3D)
that can be displayed using a scatterplot. In contrast, topic modeling aims to
discover topics from text, but for visualization, one needs to perform a
post-hoc embedding using dimensionality reduction methods. Recent approaches
propose using a generative model to jointly find topics and visualization,
allowing the semantics to be infused in the visualization space for a
meaningful interpretation. A major challenge that prevents these methods from
being used practically is the scalability of their inference algorithms. We
present, to the best of our knowledge, the first fast Auto-Encoding Variational
Bayes based inference method for jointly inferring topics and visualization.
Since our method is black box, it can handle model changes efficiently with
little mathematical rederivation effort. We demonstrate the efficiency and
effectiveness of our method on real-world large datasets and compare it with
existing baselines.
