27 citations · 59 across the 19 of their papers we have counts for
19 papers
Neuromorphic LiDAR-based Bird's Eye View Object Detection using Energy-efficient Spiking Neural Networks
Sambit Mohapatra, Senthil Yogamani, Heinrich Gotzig +1
Autonomous driving perception demands accurate and efficient processing of three-dimensional sensor data under strict power constraints. Traditional convolutional neural networks a…
Efficient Brood Cell Detection in Layer Trap Nests for Bees and Wasps: Balancing Labeling Effort and Species Coverage
Chenchang Liu, Felix Fornoff, Annika Grasreiner +3
Monitoring cavity-nesting wild bees and wasps is vital for biodiversity research and conservation. Layer trap nests (LTNs) are emerging as a valuable tool to study the abundance an…
Temporal Convolution Derived Multi-Layered Reservoir Computing
Johannes Viehweg, Dominik Walther, Patrick Mäder
The prediction of time series is a challenging task relevant in such diverse applications as analyzing financial data, forecasting flow dynamics or understanding biological process…
Privacy Preserving Federated Learning with Convolutional Variational Bottlenecks
Daniel Scheliga, Patrick Mäder, Marco Seeland
Gradient inversion attacks are an ubiquitous threat in federated learning as they exploit gradient leakage to reconstruct supposedly private training data. Recent work has proposed…
LiDAR-BEVMTN: Real-Time LiDAR Bird's-Eye View Multi-Task Perception Network for Autonomous Driving
Sambit Mohapatra, Senthil Yogamani, Varun Ravi Kumar +3
LiDAR is crucial for robust 3D scene perception in autonomous driving. LiDAR perception has the largest body of literature after camera perception. However, multi-task learning acr…
A Static Analysis Platform for Investigating Security Trends in Repositories
Tim Sonnekalb, Christopher-Tobias Knaust, Bernd Gruner +5
Static analysis tools come in many forms andconfigurations, allowing them to handle various tasks in a (secure) development process: code style linting, bug/vulnerability detection…