Visual analytics for machine learning has recently evolved as one of the most exciting areas
in the field of visualization. To better identify which research topics are promising and to …

Abstract Machine learning (ML) models are nowadays used in complex applications in
various domains, such as medicine, bioinformatics, and other sciences. Due to their black …

Massive simulations and arrays of sensing devices, in combination with increasing
computing resources, have generated large, complex, high-dimensional datasets used to …

Visual analytics systems combine machine learning or other analytic techniques with
interactive data visualization to promote sensemaking and analytical reasoning. It is through …

Visualization and visual analysis play important roles in exploring, analyzing, and
presenting scientific data. In many disciplines, data and model scenarios are becoming …

Various case studies in different application domains have shown the great potential of
visual parameter space analysis to support validating and using simulation models. In order …

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 …

Regression models play a key role in many application domains for analyzing or predicting
a quantitative dependent variable based on one or more independent variables. Automated …

Predictive analytics embraces an extensive range of techniques including statistical
modeling, machine learning, and data mining and is applied in business intelligence, public …