181 citations · 661 across the 28 of their papers we have counts for
16 papers · 1 filter
Efficient algorithms for regularized Poisson Non-negative Matrix Factorization
Nathanaël Perraudin, Adrien Teutrie, Cécile Hébert +1
We consider the problem of regularized Poisson Non-negative Matrix Factorization (NMF) problem, encompassing various regularization terms such as Lipschitz and relatively smooth fu…
An evaluation of deep learning models for predicting water depth evolution in urban floods
Stefania Russo, Nathanaël Perraudin, Steven Stalder +4
In this technical report we compare different deep learning models for prediction of water depth rasters at high spatial resolution. Efficient, accurate, and fast methods for water…
Diffusion Models for Graphs Benefit From Discrete State Spaces
Kilian Konstantin Haefeli, Karolis Martinkus, Nathanaël Perraudin +1
Denoising diffusion probabilistic models and score-matching models have proven to be very powerful for generative tasks. While these approaches have also been applied to the genera…
SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generators
Karolis Martinkus, Andreas Loukas, Nathanaël Perraudin +1
We approach the graph generation problem from a spectral perspective by first generating the dominant parts of the graph Laplacian spectrum and then building a graph matching these…
DeepSphere: a graph-based spherical CNN
Michaël Defferrard, Martino Milani, Frédérick Gusset +1
Designing a convolution for a spherical neural network requires a delicate tradeoff between efficiency and rotation equivariance. DeepSphere, a method based on a graph representati…
Scalable Graph Networks for Particle Simulations
Karolis Martinkus, Aurelien Lucchi, Nathanaël Perraudin
Learning system dynamics directly from observations is a promising direction in machine learning due to its potential to significantly enhance our ability to understand physical sy…
Discriminative structural graph classification
Younjoo Seo, Andreas Loukas, Nathanaël Perraudin
This paper focuses on the discrimination capacity of aggregation functions: these are the permutation invariant functions used by graph neural networks to combine the features of n…
DeepSphere: towards an equivariant graph-based spherical CNN
Michaël Defferrard, Nathanaël Perraudin, Tomasz Kacprzak +1
Spherical data is found in many applications. By modeling the discretized sphere as a graph, we can accommodate non-uniformly distributed, partial, and changing samplings. Moreover…
A domain agnostic measure for monitoring and evaluating GANs
Paulina Grnarova, Kfir Y Levy, Aurelien Lucchi +4
Generative Adversarial Networks (GANs) have shown remarkable results in modeling complex distributions, but their evaluation remains an unsettled issue. Evaluations are essential f…
A Time-Vertex Signal Processing Framework
Francesco Grassi, Andreas Loukas, Nathanaël Perraudin +1
An emerging way to deal with high-dimensional non-euclidean data is to assume that the underlying structure can be captured by a graph. Recently, ideas have begun to emerge related…
Compressive Embedding and Visualization using Graphs
Johan Paratte, Nathanaël Perraudin, Pierre Vandergheynst
Visualizing high-dimensional data has been a focus in data analysis communities for decades, which has led to the design of many algorithms, some of which are now considered refere…
Stationary time-vertex signal processing
Andreas Loukas, Nathanaël Perraudin
This paper considers regression tasks involving high-dimensional multivariate processes whose structure is dependent on some {known} graph topology. We put forth a new definition o…
Towards stationary time-vertex signal processing
Nathanael Perraudin, Andreas Loukas, Francesco Grassi +1
Graph-based methods for signal processing have shown promise for the analysis of data exhibiting irregular structure, such as those found in social, transportation, and sensor netw…
Tracking Time-Vertex Propagation using Dynamic Graph Wavelets
Francesco Grassi, Nathanael Perraudin, Benjamin Ricaud
Graph Signal Processing generalizes classical signal processing to signal or data indexed by the vertices of a weighted graph. So far, the research efforts have been focused on sta…
Compressive PCA for Low-Rank Matrices on Graphs
Nauman Shahid, Nathanael Perraudin, Gilles Puy +1
We introduce a novel framework for an approxi- mate recovery of data matrices which are low-rank on graphs, from sampled measurements. The rows and columns of such matrices belong…
UNLocBoX: A MATLAB convex optimization toolbox for proximal-splitting methods
Nathanael Perraudin, Vassilis Kalofolias, David Shuman +1
Convex optimization is an essential tool for machine learning, as many of its problems can be formulated as minimization problems of specific objective functions. While there is a…