Bundling techniques provide a visual simplification of a graph drawing or trail set, by
spatially grouping similar graph edges or trails. This way, the structure of the visualization …

In machine learning, pattern classification assigns high-dimensional vectors (observations)
to classes based on generalization from examples. Artificial neural networks currently …

Although deep neural networks have achieved state-of-the-art performance in several
artificial intelligence applications in the past decade, they are still hard to understand. In …

Shared autonomy systems enhance people's abilities to perform activities of daily living
using robotic manipulators. Recent systems succeed by first identifying their operators' …

We propose a new graph layout method based on a modification of the t‐distributed
Stochastic Neighbor Embedding (t‐SNE) dimensionality reduction technique. Although t …

In this paper, we investigate Confluent Drawings (CD), a technique for bundling edges in
node-link diagrams based on network connectivity. Edge-bundling techniques are designed …

Single-cell atlases pose daunting computational challenges pertaining to the integration of
spatial and temporal information and the visualization of trajectories across large atlases …

Origin–destination flow maps are a popular option to visualize connections between
different spatial locations, where specific routes between the origin and destination are …

Edge bundling techniques provide a visual simplification of cluttered graph drawings or trail
sets. While many bundling techniques exist, only few recent ones can handle large datasets …

Scatterplot matrices (SPLOMs) are widely used for exploring multidimensional data.
Scatterplot diagnostics (scagnostics) approaches measure characteristics of scatterplots to …