research
∙
08/21/2023
Approximately Equivariant Graph Networks
Graph neural networks (GNNs) are commonly described as being permutation...
share
research
∙
06/06/2023
Fine-grained Expressivity of Graph Neural Networks
Numerous recent works have analyzed the expressive power of message-pass...
research
∙
05/25/2023
A graphon-signal analysis of graph neural networks
We present an approach for analyzing message passing graph neural networ...
research
∙
11/28/2022
On the Effective Usage of Priors in RSS-based Localization
In this paper, we study the localization problem in dense urban settings...
research
∙
11/22/2022
Explaining Image Classifiers with Multiscale Directional Image Representation
Image classifiers are known to be difficult to interpret and therefore r...
research
∙
11/18/2022
Dataset of Pathloss and ToA Radio Maps With Localization Application
In this article, we present a collection of radio map datasets in dense ...
research
∙
10/15/2022
Unveiling the Sampling Density in Non-Uniform Geometric Graphs
A powerful framework for studying graphs is to consider them as geometri...
research
∙
06/11/2022
Memorization-Dilation: Modeling Neural Collapse Under Noise
The notion of neural collapse refers to several emergent phenomena that ...
research
∙
02/01/2022
LocUNet: Fast Urban Positioning Using Radio Maps and Deep Learning
This paper deals with the problem of localization in a cellular network ...
research
∙
02/01/2022
Stability and Generalization Capabilities of Message Passing Graph Neural Networks
Message passing neural networks (MPNN) have seen a steep rise in popular...
research
∙
10/12/2021
A Rate-Distortion Framework for Explaining Black-box Model Decisions
We present the Rate-Distortion Explanation (RDE) framework, a mathematic...
research
∙
10/07/2021
Cartoon Explanations of Image Classifiers
We present CartoonX (Cartoon Explanation), a novel model-agnostic explan...
research
∙
09/21/2021
Transferability of Graph Neural Networks: an Extended Graphon Approach
We study spectral graph convolutional neural networks (GCNNs), where fil...
research
∙
08/26/2021
Existence of Uncertainty Minimizers for the Continuous Wavelet Transform
Continuous wavelet design is the endeavor to construct mother wavelets w...
research
∙
06/23/2021
Real-time Outdoor Localization Using Radio Maps: A Deep Learning Approach
This paper deals with the problem of localization in a cellular network ...
research
∙
04/04/2021
Wavelet Design with Optimally Localized Ambiguity Function: a Variational Approach
In this paper, we design mother wavelets for the 1D continuous wavelet t...
research
∙
11/03/2020
Quasi Monte Carlo Time-Frequency Analysis
We study signal processing tasks in which the signal is mapped via some ...
research
∙
09/20/2020
Randomized Continuous Frames in Time-Frequency Analysis
Recently, a Monte Carlo approach was proposed for speeding up signal pro...
research
∙
07/01/2020
In-Distribution Interpretability for Challenging Modalities
It is widely recognized that the predictions of deep neural networks are...
research
∙
06/09/2020
Real-time Localization Using Radio Maps
This paper deals with the problem of localization in a cellular network ...
research
∙
11/17/2019
RadioUNet: Fast Radio Map Estimation with Convolutional Neural Networks
In this paper we propose a highly efficient and very accurate method for...
research
∙
07/30/2019
Transferability of Spectral Graph Convolutional Neural Networks
This paper focuses on spectral graph convolutional neural networks (Conv...
research
∙
01/29/2019
On the Transferability of Spectral Graph Filters
This paper focuses on spectral filters on graphs, namely filters defined...
research
∙
12/07/2017