activity
20192026
most citedexplAIner: A Visual Analytics Framework for Interactive and Explainable Machine Learning

262 citations · 272 across the 13 of their papers we have counts for

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5 papers · 1 filter

cs.HC2026

Visual Boosting Techniques for Spatiotemporal Dense Pixel Visualizations

Julius Rauscher, Frederik L. Dennig, Udo Schlegel +2

The analysis of spatiotemporal data is essential in domains such as epidemiology and environmental monitoring, where understanding the interplay between spatially distributed pheno…

cs.HC2023

Visual Explanations with Attributions and Counterfactuals on Time Series Classification

Udo Schlegel, Daniela Oelke, Daniel A. Keim +1

With the rising necessity of explainable artificial intelligence (XAI), we see an increase in task-dependent XAI methods on varying abstraction levels. XAI techniques on a global l…

cs.HC2020

Multiscale Snapshots: Visual Analysis of Temporal Summaries in Dynamic Graphs

Eren Cakmak, Udo Schlegel, Dominik Jäckle +2

The overview-driven visual analysis of large-scale dynamic graphs poses a major challenge. We propose Multiscale Snapshots, a visual analytics approach to analyze temporal summarie…

cs.HC2020★ 2 cited

SpatialRugs: Enhancing Spatial Awareness of Movement in Dense Pixel Visualizations

Juri F. Buchmüller, Udo Schlegel, Eren Cakmak +2

Compact visual summaries of spatio-temporal movement data often strive to express accurate positions of movers. We present SpatialRugs, a technique to enhance the spatial awareness…

cs.HC2019★ 262 cited

explAIner: A Visual Analytics Framework for Interactive and Explainable Machine Learning

Thilo Spinner, Udo Schlegel, Hanna Schäfer +1

We propose a framework for interactive and explainable machine learning that enables users to (1) understand machine learning models; (2) diagnose model limitations using different…