8 citations · 17 across the 8 of their papers we have counts for
11 papers
Driving on Memory
Christian Löwens, Thorben Funke, Alexandru Paul Condurache
End-to-end autonomous driving models plan future trajectories from raw sensor input. While earlier driving benchmarks often measured deviation from the human trajectory, current be…
PseudoMapTrainer: Learning Online Mapping without HD Maps
Christian Löwens, Thorben Funke, Jingchao Xie +1
Online mapping models show remarkable results in predicting vectorized maps from multi-view camera images only. However, all existing approaches still rely on ground-truth high-def…
Unsupervised Point Cloud Registration with Self-Distillation
Christian Löwens, Thorben Funke, André Wagner +1
Rigid point cloud registration is a fundamental problem and highly relevant in robotics and autonomous driving. Nowadays deep learning methods can be trained to match a pair of poi…
W-Trace: Robust and Effective Watermarking for GPS Trajectories
Rajjat Dadwal, Thorben Funke, Michael Nüsken +1
With the rise of data-driven methods for traffic forecasting, accident prediction, and profiling driving behavior, personal GPS trajectory data has become an essential asset for bu…
BAGEL: A Benchmark for Assessing Graph Neural Network Explanations
Mandeep Rathee, Thorben Funke, Avishek Anand +1
The problem of interpreting the decisions of machine learning is a well-researched and important. We are interested in a specific type of machine learning model that deals with gra…
Private Graph Extraction via Feature Explanations
Iyiola E. Olatunji, Mandeep Rathee, Thorben Funke +1
Privacy and interpretability are two important ingredients for achieving trustworthy machine learning. We study the interplay of these two aspects in graph machine learning through…
Releasing Graph Neural Networks with Differential Privacy Guarantees
Iyiola E. Olatunji, Thorben Funke, Megha Khosla
With the increasing popularity of graph neural networks (GNNs) in several sensitive applications like healthcare and medicine, concerns have been raised over the privacy aspects of…
An Adaptive Clustering Approach for Accident Prediction
Rajjat Dadwal, Thorben Funke, Elena Demidova
Traffic accident prediction is a crucial task in the mobility domain. State-of-the-art accident prediction approaches are based on static and uniform grid-based geospatial aggregat…
Learnt Sparsification for Interpretable Graph Neural Networks
Mandeep Rathee, Zijian Zhang, Thorben Funke +2
Graph neural networks (GNNs) have achieved great success on various tasks and fields that require relational modeling. GNNs aggregate node features using the graph structure as ind…
Zorro: Valid, Sparse, and Stable Explanations in Graph Neural Networks
Thorben Funke, Megha Khosla, Mandeep Rathee +1
With the ever-increasing popularity and applications of graph neural networks, several proposals have been made to explain and understand the decisions of a graph neural network. E…
Low-dimensional statistical manifold embedding of directed graphs
Thorben Funke, Tian Guo, Alen Lancic +1
We propose a novel node embedding of directed graphs to statistical manifolds, which is based on a global minimization of pairwise relative entropy and graph geodesics in a non-lin…