GTC Silicon Valley-2019 ID:S9140:Data2Vis: Automatic Generation of Data Visualizations Using Sequence-to-Sequence Recurrent Neural Networks.
Victor Dibia(Cloudera Fast Forward Labs)
We'll introduce Data2Vis, a neural translation model for automatically generating visualizations from given datasets. We formulate visualization generation as a sequence-to-sequence translation problem in which data is mapped to visualization specifications in a declarative language. We'll discuss how we train a multilayered attention-based encoder-decoder model on a corpus of visualization specifications. Qualitative results show that the model learns the vocabulary and syntax for valid visualization specifications, appropriate transformations, and how to use common data-selection patterns occurring within data visualizations. We'll describe how Data2Vis generates high-quality visualizations comparable to manual efforts in a fraction of the time, and how it has the potential to learn more complex visualization strategies at scale. We will also provide guidance on training such a model using the Cloudera Datascience Workbench and explore uses for Data2Vis within visualization tools.