24 citations · 35 across the 14 of their papers we have counts for
15 papers
CoRe-GNN: Multilevel Message passing on Coarsened graphs
Antonin Joly, Nicolas Keriven, Aline Roumy
Training Graph Neural Networks on large graphs is challenged by the memory cost of storing all node representations across layers. We show that several existing scalable approaches…
Discovering shared interpretable operations in image compression autoencoders
Caroline Mazini Rodrigues, Nicolas Keriven, Thomas Maugey
With the increasing adoption of deep learning for applications such as image compression, improvements in the rate-distortion trade-off have been achieved at the cost of increasing…
Statistical Consistency of Discrete-to-Continuous Limits of Determinantal Point Processes
Hugo Jaquard, Nicolas Keriven
We investigate the limiting behavior of discrete determinantal point processes (DPPs) towards continuous DPPs when the size of the set to sample from goes to infinity. We propose a…
Efficient training for compact compression models via sequential distillation
Caroline Mazini Rodrigues, Nicolas Keriven, Thomas Maugey
Deep learning models for image compression often face practical limitations in hardware-constrained applications. Although these models achieve high-quality reconstructions, they a…
Taxonomy of reduction matrices for Graph Coarsening
Antonin Joly, Nicolas Keriven, Aline Roumy
Graph coarsening aims to diminish the size of a graph to lighten its memory footprint, and has numerous applications in graph signal processing and machine learning. It is usually…
Backward Oversmoothing: why is it hard to train deep Graph Neural Networks?
Nicolas Keriven
Oversmoothing has long been identified as a major limitation of Graph Neural Networks (GNNs): input node features are smoothed at each layer and converge to a non-informative repre…
Node Regression on Latent Position Random Graphs via Local Averaging
Martin Gjorgjevski, Nicolas Keriven, Simon Barthelmé +1
Node regression consists in predicting the value of a graph label at a node, given observations at the other nodes. To gain some insight into the performance of various estimators…
Graph Coarsening with Message-Passing Guarantees
Antonin Joly, Nicolas Keriven
Graph coarsening aims to reduce the size of a large graph while preserving some of its key properties, which has been used in many applications to reduce computational load and mem…
What functions can Graph Neural Networks compute on random graphs? The role of Positional Encoding
Nicolas Keriven, Samuel Vaiter
We aim to deepen the theoretical understanding of Graph Neural Networks (GNNs) on large graphs, with a focus on their expressive power. Existing analyses relate this notion to the…
Convergence of Message Passing Graph Neural Networks with Generic Aggregation On Large Random Graphs
Matthieu Cordonnier, Nicolas Keriven, Nicolas Tremblay +1
We study the convergence of message passing graph neural networks on random graph models to their continuous counterpart as the number of nodes tends to infinity. Until now, this c…
Gradient scarcity with Bilevel Optimization for Graph Learning
Hashem Ghanem, Samuel Vaiter, Nicolas Keriven
A common issue in graph learning under the semi-supervised setting is referred to as gradient scarcity. That is, learning graphs by minimizing a loss on a subset of nodes causes ed…
Stability of Entropic Wasserstein Barycenters and application to random geometric graphs
Marc Theveneau, Nicolas Keriven
As interest in graph data has grown in recent years, the computation of various geometric tools has become essential. In some area such as mesh processing, they often rely on the c…
Not too little, not too much: a theoretical analysis of graph (over)smoothing
Nicolas Keriven
We analyze graph smoothing with \emph{mean aggregation}, where each node successively receives the average of the features of its neighbors. Indeed, it has quickly been observed th…
Entropic Optimal Transport in Random Graphs
Nicolas Keriven
In graph analysis, a classic task consists in computing similarity measures between (groups of) nodes. In latent space random graphs, nodes are associated to unknown latent variabl…
Supervised learning of analysis-sparsity priors with automatic differentiation
Hashem Ghanem, Joseph Salmon, Nicolas Keriven +1
Sparsity priors are commonly used in denoising and image reconstruction. For analysis-type priors, a dictionary defines a representation of signals that is likely to be sparse. In…