Visualizations themselves have become a data format. Akin to other data formats such as
text and images, visualizations are increasingly created, stored, shared, and (re-) used with …

Knowledge of human perception has long been incorporated into visualizations to enhance
their quality and effectiveness. The last decade, in particular, has shown an increase in …

A proposal that we think about digital technologies such as machine learning not in terms of
artificial intelligence but as artificial communication. Algorithms that work with deep learning …

High-quality visualization collections are beneficial for a variety of applications including
visualization reference and data-driven visualization design. The visualization community …

In this paper, we list the goals for and the pros and cons of guidance, and we discuss the
role that it can play not only in key low-level visualization tasks but also the more …

Pictorial visualizations portray data with figurative messages and approximate the audience
to the visualization. Previous research on pictorial visualizations has developed authoring …

Topic models are a class of unsupervised learning algorithms for detecting the semantic
structure within a text corpus. Together with a subsequent dimensionality reduction …

Visualizations often encode numeric data using sequential and diverging color ramps.
Effective ramps use colors that are sufficiently discriminable, align well with the data, and are …

Translating natural language to visualization (NL2VIS) has shown great promise for visual
data analysis, but it remains a challenging task that requires multiple low-level …

Supervised machine learning techniques require labelled multivariate training datasets.
Many approaches address the issue of unlabelled datasets by tightly coupling machine …