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 …

Visual analytics systems integrate interactive visualizations and machine learning to enable
expert users to solve complex analysis tasks. Applications combine techniques from various …

Visualization and artificial intelligence (AI) are well-applied approaches to data analysis. On
one hand, visualization can facilitate humans in data understanding through intuitive visual …

In machine learning (ML), ensemble methods-such as bagging, boosting, and stacking-are
widely-established approaches that regularly achieve top-notch predictive performance …

Machine learning models have been widely adopted in several fields. However, most recent
studies have shown several vulnerabilities from attacks with a potential to jeopardize the …

The past decade has witnessed a plethora of works that leverage the power of visualization
(VIS) to interpret machine learning (ML) models. The corresponding research topic, VIS4ML …

Deep learning, a robust framework for complex learning, outperforms previous machine
learning approaches and finds widespread use. However, security vulnerabilities, especially …

Semantic segmentation is a critical component in autonomous driving and has to be
thoroughly evaluated due to safety concerns. Deep neural network (DNN) based semantic …

Recent advances in machine learning have created an opportunity to embed artificial
intelligence in software-intensive systems. These artificial intelligence systems, however …