The use and creation of machine‐learning‐based solutions to solve problems or reduce
their computational costs are becoming increasingly widespread in many domains. Deep …

Research in machine learning has become very popular in recent years, with many types of
models proposed to comprehend and predict patterns and trends in data originating from …

Dimensionality Reduction (DR) is a core building block in visualizing multidimensional data.
For DR techniques to be useful in exploratory data analysis, they need to be adapted to …

Time is an exceptional dimension with high relevance in medicine, engineering, business,
science, biography, history, planning, or project management. Understanding time-oriented …

Over the last decade, ensemble visualization has witnessed a significant development due
to the wide availability of ensemble data, and the increasing visualization needs from a …

There is fast‐growing literature on provenance‐related research, covering aspects such as
its theoretical framework, use cases, and techniques for capturing, visualizing, and …

We present explorable multiverse analysis reports, a new approach to statistical reporting
where readers of research papers can explore alternative analysis options by interacting …

Clustering, the process of grou** together similar items into distinct partitions, is a
common type of unsupervised machine learning that can be useful for summarizing and …

We propose InSituNet, a deep learning based surrogate model to support parameter space
exploration for ensemble simulations that are visualized in situ. In situ visualization …

An increasing number of interactive visualization tools stress the integration with
computational software like MATLAB and R to access a variety of proven algorithms. In …