For many data analysis tasks, such as the formation of well-balanced groups for a fair race
or collaboration in learning settings, the balancing between data attributes is at least as …

Exploring high-dimensional data is a common task in many scientific disciplines. To address
this task, two-dimensional embeddings, such as tSNE and UMAP, are widely used. While …

We present a neural optimization model trained with reinforcement learning to solve the
coordinate ordering problem for sets of star glyphs. Given a set of star glyphs associated to …

Ordering has been extensively studied in many visualization applications, such as axis and
matrix reordering, for the simple reason that the order will greatly impact the perceived …

Glyph-based visualization is one of the main techniques for visualizing complex multivariate
data. With small glyphs, data variables are typically encoded with relatively low visual and …

Similarity detection seeks to identify similar, but distinct items over multivariate datasets.
Often, similarity cannot be defined computationally, leading to a need for visual analysis …

Recently, deep reinforcement learning has become very successful in making complex
decisions, achieving super-human performance in Go, chess, and challenging video games …

In the era of big data, the continuous generation of data from various fields has resulted in
large and complex datasets. These datasets often come in diverse formats and structures …

Musical scores are frequently annotated with harmonic information, but widely used text-
based methods rely on a limited number of visual channels. Though glyph-based methods …

Honey bees play an important role in pollinating major crops and fruits across the world.
Unfortunately, in recent years, many honey bee colonies have been lost due to various …