4 citations · 5 across the 4 of their papers we have counts for
8 papers
ViNNPruner: Visual Interactive Pruning for Deep Learning
Udo Schlegel, Samuel Schiegg, Daniel A. Keim
Neural networks grow vastly in size to tackle more sophisticated tasks. In many cases, such large networks are not deployable on particular hardware and need to be reduced in size.…
Time Series Model Attribution Visualizations as Explanations
Udo Schlegel, Daniel A. Keim
Attributions are a common local explanation technique for deep learning models on single samples as they are easily extractable and demonstrate the relevance of input values. In ma…
TS-MULE: Local Interpretable Model-Agnostic Explanations for Time Series Forecast Models
Udo Schlegel, Duy Vo Lam, Daniel A. Keim +1
Time series forecasting is a demanding task ranging from weather to failure forecasting with black-box models achieving state-of-the-art performances. However, understanding and de…
An Empirical Study of Explainable AI Techniques on Deep Learning Models For Time Series Tasks
Udo Schlegel, Daniela Oelke, Daniel A. Keim +1
Decision explanations of machine learning black-box models are often generated by applying Explainable AI (XAI) techniques. However, many proposed XAI methods produce unverified ou…
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…
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…