22 citations · 38 across the 5 of their papers we have counts for
5 papers
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…
Radar Image Reconstruction from Raw ADC Data using Parametric Variational Autoencoder with Domain Adaptation
Michael Stephan, Thomas Stadelmayer, Avik Santra +3
This paper presents a parametric variational autoencoder-based human target detection and localization framework working directly with the raw analog-to-digital converter data from…
Cross-modal Learning of Graph Representations using Radar Point Cloud for Long-Range Gesture Recognition
Souvik Hazra, Hao Feng, Gamze Naz Kiprit +5
Gesture recognition is one of the most intuitive ways of interaction and has gathered particular attention for human computer interaction. Radar sensors possess multiple intrinsic…
Label-Aware Ranked Loss for robust People Counting using Automotive in-cabin Radar
Lorenzo Servadei, Huawei Sun, Julius Ott +6
In this paper, we introduce the Label-Aware Ranked loss, a novel metric loss function. Compared to the state-of-the-art Deep Metric Learning losses, this function takes advantage o…