4 citations · 4 across the 5 of their papers we have counts for
7 papers
Graphs as Tools to Improve Deep Learning Methods
Carlos Lassance, Myriam Bontonou, Mounia Hamidouche +3
In recent years, deep neural networks (DNNs) have known an important rise in popularity. However, although they are state-of-the-art in many machine learning challenges, they still…
Improving Classification Accuracy with Graph Filtering
Mounia Hamidouche, Carlos Lassance, Yuqing Hu +3
In machine learning, classifiers are typically susceptible to noise in the training data. In this work, we aim at reducing intra-class noise with the help of graph filtering to imp…
Ranking Deep Learning Generalization using Label Variation in Latent Geometry Graphs
Carlos Lassance, Louis Béthune, Myriam Bontonou +2
Measuring the generalization performance of a Deep Neural Network (DNN) without relying on a validation set is a difficult task. In this work, we propose exploiting Latent Geometry…
Spectral bounds of the regularized normalized Laplacian for random geometric graphs
Mounia Hamidouche, Laura Cottatellucci, Konstantin Avrachenkov
In this work, we study the spectrum of the regularized normalized Laplacian for random geometric graphs (RGGs) in both the connectivity and thermodynamic regimes. We prove that the…
Spectral Analysis of the Adjacency Matrix of Random Geometric Graphs
Mounia Hamidouche, Laura Cottatellucci, Konstantin Avrachenkov
In this article, we analyze the limiting eigenvalue distribution (LED) of random geometric graphs (RGGs). The RGG is constructed by uniformly distributing nodes on the -dime…
Eigenvalues and Spectral Dimension of Random Geometric Graphs in Thermodynamic Regime
Konstantin Avrachenkov, Laura Cottatellucci, Mounia Hamidouche
Network geometries are typically characterized by having a finite spectral dimension (SD), that characterizes the return time distribution of a random walk on a graph. The…