most citedSignal inference with unknown response: Calibration-uncertainty renormalized estimator

4 citations · 8 across the 5 of their papers we have counts for

collaborators

5 papers

cs.AI2023

Prediction and Interpretation of Vehicle Trajectories in the Graph Spectral Domain

Marion Neumeier, Sebastian Dorn, Michael Botsch +1

This work provides a comprehensive analysis and interpretation of the graph spectral representation of traffic scenarios. Based on a spatio-temporal vehicle interaction graph, an o…

cs.LG2023

Optimization and Interpretability of Graph Attention Networks for Small Sparse Graph Structures in Automotive Applications

Marion Neumeier, Andreas Tollkühn, Sebastian Dorn +2

For automotive applications, the Graph Attention Network (GAT) is a prominently used architecture to include relational information of a traffic scenario during feature embedding.…

cs.LG20231 cited

Gradient Derivation for Learnable Parameters in Graph Attention Networks

Marion Neumeier, Andreas Tollkühn, Sebastian Dorn +2

This work provides a comprehensive derivation of the parameter gradients for GATv2 [4], a widely used implementation of Graph Attention Networks (GATs). GATs have proven to be powe…

astro-ph.CO20143 cited

All-sky reconstruction of the primordial scalar potential from WMAP temperature data

Sebastian Dorn, Maksim Greiner, Torsten A. Enßlin

An essential quantity required to understand the physics of the early Universe, in particular the inflationary epoch, is the primordial scalar potential and its statistics. We…

physics.data-an20144 cited

Signal inference with unknown response: Calibration-uncertainty renormalized estimator

Sebastian Dorn, Torsten A. Enßlin, Maksim Greiner +2

The calibration of a measurement device is crucial for every scientific experiment, where a signal has to be inferred from data. We present CURE, the calibration uncertainty renorm…