262 citations · 272 across the 13 of their papers we have counts for
16 papers
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…
Leveraging Color Channel Independence for Improved Unsupervised Object Detection
Bastian Jäckl, Yannick Metz, Udo Schlegel +2
Object-centric architectures can learn to extract distinct object representations from visual scenes, enabling downstream applications on the object level. Similarly to autoencoder…
Interactive dense pixel visualizations for time series and model attribution explanations
Udo Schlegel, Daniel A. Keim
The field of Explainable Artificial Intelligence (XAI) for Deep Neural Network models has developed significantly, offering numerous techniques to extract explanations from models.…
Interactive Counterfactual Generation for Univariate Time Series
Udo Schlegel, Julius Rauscher, Daniel A. Keim
We propose an interactive methodology for generating counterfactual explanations for univariate time series data in classification tasks by leveraging 2D projections and decision b…
Finding the DeepDream for Time Series: Activation Maximization for Univariate Time Series
Udo Schlegel, Daniel A. Keim, Tobias Sutter
Understanding how models process and interpret time series data remains a significant challenge in deep learning to enable applicability in safety-critical areas such as healthcare…
Introducing the Attribution Stability Indicator: a Measure for Time Series XAI Attributions
Udo Schlegel, Daniel A. Keim
Given the increasing amount and general complexity of time series data in domains such as finance, weather forecasting, and healthcare, there is a growing need for state-of-the-art…
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…
A Deep Dive into Perturbations as Evaluation Technique for Time Series XAI
Udo Schlegel, Daniel A. Keim
Explainable Artificial Intelligence (XAI) has gained significant attention recently as the demand for transparency and interpretability of machine learning models has increased. In…
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…
Towards a Rigorous Evaluation of XAI Methods on Time Series
Udo Schlegel, Hiba Arnout, Mennatallah El-Assady +2
Explainable Artificial Intelligence (XAI) methods are typically deployed to explain and debug black-box machine learning models. However, most proposed XAI methods are black-boxes…
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…