2 papers
cs.LG2024
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…
cs.LG2024
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…