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cs.LG2025
GenFacts-Generative Counterfactual Explanations for Multi-Variate Time Series
Sarah Seifi, Anass Ibrahimi, Tobias Sukianto +3
Counterfactual explanations aim to enhance model transparency by showing how inputs can be minimally altered to change predictions. For multivariate time series, existing methods o…
cs.LG2025
Learning Interpretable Rules from Neural Networks: Neurosymbolic AI for Radar Hand Gesture Recognition
Sarah Seifi, Tobias Sukianto, Cecilia Carbonelli +2
Rule-based models offer interpretability but struggle with complex data, while deep neural networks excel in performance yet lack transparency. This work investigates a neuro-symbo…
cs.LG2023
Detection of Sensor-To-Sensor Variations using Explainable AI
Sarah Seifi, Sebastian A. Schober, Cecilia Carbonelli +2
With the growing concern for air quality and its impact on human health, interest in environmental gas monitoring has increased. However, chemi-resistive gas sensing devices are pl…