activity
20142024
most citedLearning the MMSE Channel Estimator

318 citations · 1k across the 84 of their papers we have counts for

collaborators
Showing cs.LGShow all

6 papers · 1 filter

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.LG2023★ 11 cited

A Multidimensional Graph Fourier Transformation Neural Network for Vehicle Trajectory Prediction

Marion Neumeier, Andreas Tollkühn, Michael Botsch +1

This work introduces the multidimensional Graph Fourier Transformation Neural Network (GFTNN) for long-term trajectory predictions on highways. Similar to Graph Neural Networks (GN…

cs.LG2023★ 1 cited

On Learning the Tail Quantiles of Driving Behavior Distributions via Quantile Regression and Flows

Jia Yu Tee, Oliver De Candido, Wolfgang Utschick +1

Towards safe autonomous driving (AD), we consider the problem of learning models that accurately capture the diversity and tail quantiles of human driver behavior probability distr…

cs.LG2023★ 1 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…

cs.LG2021★ 2 cited

Classification and Uncertainty Quantification of Corrupted Data using Semi-Supervised Autoencoders

Philipp Joppich, Sebastian Dorn, Oliver De Candido +2

Parametric and non-parametric classifiers often have to deal with real-world data, where corruptions like noise, occlusions, and blur are unavoidable - posing significant challenge…

cs.LG2020★ 5 cited

An Entropy Based Outlier Score and its Application to Novelty Detection for Road Infrastructure Images

Jonas Wurst, Alberto Flores Fernández, Michael Botsch +1

A novel unsupervised outlier score, which can be embedded into graph based dimensionality reduction techniques, is presented in this work. The score uses the directed nearest neigh…