5 citations · 13 across the 15 of their papers we have counts for
5 papers · 1 filter
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…
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…
A Rate-Distortion Framework for Explaining Black-box Model Decisions
Stefan Kolek, Duc Anh Nguyen, Ron Levie +2
We present the Rate-Distortion Explanation (RDE) framework, a mathematically well-founded method for explaining black-box model decisions. The framework is based on perturbations o…
In-Distribution Interpretability for Challenging Modalities
Cosmas Heiß, Ron Levie, Cinjon Resnick +2
It is widely recognized that the predictions of deep neural networks are difficult to parse relative to simpler approaches. However, the development of methods to investigate the m…
On the Transferability of Spectral Graph Filters
Ron Levie, Elvin Isufi, Gitta Kutyniok
This paper focuses on spectral filters on graphs, namely filters defined as elementwise multiplication in the frequency domain of a graph. In many graph signal processing settings,…