70 citations · 110 across the 33 of their papers we have counts for
7 papers · 1 filter
Dataset of Pathloss and ToA Radio Maps With Localization Application
Çağkan Yapar, Ron Levie, Gitta Kutyniok +1
In this article, we present a collection of radio map datasets in dense urban setting, which we generated and made publicly available. The datasets include simulated pathloss/recei…
On the Effective Usage of Priors in RSS-based Localization
Çağkan Yapar, Fabian Jaensch, Ron Levie +1
In this paper, we study the localization problem in dense urban settings. In such environments, Global Navigation Satellite Systems fail to provide good accuracy due to low likelih…
Unveiling the Sampling Density in Non-Uniform Geometric Graphs
Raffaele Paolino, Aleksandar Bojchevski, Stephan Günnemann +2
A powerful framework for studying graphs is to consider them as geometric graphs: nodes are randomly sampled from an underlying metric space, and any pair of nodes is connected if…
Explaining Image Classifiers with Multiscale Directional Image Representation
Stefan Kolek, Robert Windesheim, Hector Andrade Loarca +2
Image classifiers are known to be difficult to interpret and therefore require explanation methods to understand their decisions. We present ShearletX, a novel mask explanation met…
Memorization-Dilation: Modeling Neural Collapse Under Label Noise
Duc Anh Nguyen, Ron Levie, Julian Lienen +2
The notion of neural collapse refers to several emergent phenomena that have been empirically observed across various canonical classification problems. During the terminal phase o…
LocUNet: Fast Urban Positioning Using Radio Maps and Deep Learning
Çağkan Yapar, Ron Levie, Gitta Kutyniok +1
This paper deals with the problem of localization in a cellular network in a dense urban scenario. Global Navigation Satellite Systems (GNSS) typically perform poorly in urban envi…
Generalization Analysis of Message Passing Neural Networks on Large Random Graphs
Sohir Maskey, Ron Levie, Yunseok Lee +1
Message passing neural networks (MPNN) have seen a steep rise in popularity since their introduction as generalizations of convolutional neural networks to graph-structured data, a…