15 citations · 18 across the 26 of their papers we have counts for
9 papers · 1 filter
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…
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…
Temporal Decisions: Leveraging Temporal Correlation for Efficient Decisions in Early Exit Neural Networks
Max Sponner, Lorenzo Servadei, Bernd Waschneck +2
Deep Learning is becoming increasingly relevant in Embedded and Internet-of-things applications. However, deploying models on embedded devices poses a challenge due to their resour…
Efficient Post-Training Augmentation for Adaptive Inference in Heterogeneous and Distributed IoT Environments
Max Sponner, Lorenzo Servadei, Bernd Waschneck +2
Early Exit Neural Networks (EENNs) present a solution to enhance the efficiency of neural network deployments. However, creating EENNs is challenging and requires specialized domai…
Temporal Patience: Efficient Adaptive Deep Learning for Embedded Radar Data Processing
Max Sponner, Julius Ott, Lorenzo Servadei +3
Radar sensors offer power-efficient solutions for always-on smart devices, but processing the data streams on resource-constrained embedded platforms remains challenging. This pape…
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…
Uncertainty-based Meta-Reinforcement Learning for Robust Radar Tracking
Julius Ott, Lorenzo Servadei, Gianfranco Mauro +3
Nowadays, Deep Learning (DL) methods often overcome the limitations of traditional signal processing approaches. Nevertheless, DL methods are barely applied in real-life applicatio…
Utilizing Explainable AI for improving the Performance of Neural Networks
Huawei Sun, Lorenzo Servadei, Hao Feng +3
Nowadays, deep neural networks are widely used in a variety of fields that have a direct impact on society. Although those models typically show outstanding performance, they have…
MEET: A Monte Carlo Exploration-Exploitation Trade-off for Buffer Sampling
Julius Ott, Lorenzo Servadei, Jose Arjona-Medina +7
Data selection is essential for any data-based optimization technique, such as Reinforcement Learning. State-of-the-art sampling strategies for the experience replay buffer improve…