262 citations · 272 across the 13 of their papers we have counts for
9 papers · 1 filter
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…
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…
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…