Nicolas Tremblay | DeepAI

DeepAI AI Chat

Featured Co-authors

Rémi Gribonval
58 publications
Pierre Vandergheynst
39 publications
Romain Couillet
38 publications
Andreas Loukas
31 publications
Gilles Puy
27 publications
Samuel Vaiter
25 publications
Nicolas Keriven
22 publications
Rémi Bardenet
19 publications
Michaël Fanuel
17 publications
Konstantin Usevich
15 publications
Pierre-Olivier Amblard
14 publications

∙ 04/21/2023

Convergence of Message Passing Graph Neural Networks with Generic Aggregation On Large Random Graphs

We study the convergence of message passing graph neural networks on ran...

0 Matthieu Cordonnier, et al. ∙

∙ 10/31/2022

A Faster Sampler for Discrete Determinantal Point Processes

Discrete Determinantal Point Processes (DPPs) have a wide array of poten...

0 Simon Barthelmé, et al. ∙

∙ 10/15/2022

Smoothing complex-valued signals on Graphs with Monte-Carlo

We introduce new smoothing estimators for complex signals on graphs, bas...

0 Hugo Jaquard, et al. ∙

∙ 01/04/2022

Gaussian Process Regression in the Flat Limit

Gaussian process (GP) regression is a fundamental tool in Bayesian stati...

0 Simon Barthelmé, et al. ∙

∙ 07/15/2021

Determinantal Point Processes in the Flat Limit

Determinantal point processes (DPPs) are repulsive point processes where...

0 Simon Barthelmé, et al. ∙

∙ 07/12/2021

Extended L-ensembles: a new representation for Determinantal Point Processes

Determinantal point processes (DPPs) are a class of repulsive point proc...

0 Nicolas Tremblay, et al. ∙

∙ 03/05/2021

Nishimori meets Bethe: a spectral method for node classification in sparse weighted graphs

This article unveils a new relation between the Nishimori temperature pa...

0 Lorenzo Dall'Amico, et al. ∙

∙ 11/20/2020

Graph Tikhonov Regularization and Interpolation via Random Spanning Forests

Novel Monte Carlo estimators are proposed to solve both the Tikhonov reg...

0 Yusuf Pilavci, et al. ∙

∙ 10/16/2020

Fast Graph Kernel with Optical Random Features

The graphlet kernel is a classical method in graph classification. It ho...

0 Hashem Ghanem, et al. ∙

∙ 06/03/2020

Community detection in sparse time-evolving graphs with a dynamical Bethe-Hessian

This article considers the problem of community detection in sparse dyna...

0 Lorenzo Dall'Amico, et al. ∙

∙ 03/20/2020

A unified framework for spectral clustering in sparse graphs

This article considers spectral community detection in the regime of spa...

0 Lorenzo Dall'Amico, et al. ∙

∙ 12/03/2019

Optimal Laplacian regularization for sparse spectral community detection

Regularization of the classical Laplacian matrices was empirically shown...

0 Lorenzo Dall'Amico, et al. ∙

∙ 10/17/2019

Smoothing graph signals via random spanning forests

Another facet of the elegant link between random processes on graphs and...

0 Yusuf Y. Pilavci, et al. ∙

∙ 05/06/2019

Estimating the inverse trace using random forests on graphs

Some data analysis problems require the computation of (regularised) inv...

0 Simon Barthelmé, et al. ∙

∙ 01/29/2019

Approximating Spectral Clustering via Sampling: a Review

Spectral clustering refers to a family of unsupervised learning algorith...

0 Nicolas Tremblay, et al. ∙

∙ 01/25/2019

Optimized Deformed Laplacian for Spectrum-based Community Detection in Sparse Heterogeneous Graphs

Spectral clustering is one of the most popular, yet still incompletely u...

0 Lorenzo Dall'Amico, et al. ∙

∙ 03/23/2018

Determinantal Point Processes for Coresets

When one is faced with a dataset too large to be used all at once, an ob...

0 Nicolas Tremblay, et al. ∙

∙ 03/05/2018

Asymptotic Equivalence of Fixed-size and Varying-size Determinantal Point Processes

Determinantal Point Processes (DPPs) are popular models for point proces...

0 Simon Barthelmé, et al. ∙

∙ 02/23/2018

Optimized Algorithms to Sample Determinantal Point Processes

In this technical report, we discuss several sampling algorithms for Det...

0 Nicolas Tremblay, et al. ∙

∙ 11/01/2017

Analyzing the Approximation Error of the Fast Graph Fourier Transform

The graph Fourier transform (GFT) is in general dense and requires O(n^2...

0 Luc LeMagoarou, et al. ∙

∙ 03/05/2017

Graph sampling with determinantal processes

We present a new random sampling strategy for k-bandlimited signals defi...

0 Nicolas Tremblay, et al. ∙

∙ 10/27/2016

Compressive K-means

The Lloyd-Max algorithm is a classical approach to perform K-means clust...

0 Nicolas Keriven, et al. ∙

∙ 02/05/2016

Compressive Spectral Clustering

Spectral clustering has become a popular technique due to its high perfo...

0 Nicolas Tremblay, et al. ∙

∙ 11/16/2015

Random sampling of bandlimited signals on graphs

We study the problem of sampling k-bandlimited signals on graphs. We pro...

0 Gilles Puy, et al. ∙

Are you a researcher?
Expose your work
to one of the largest
A.I. communities in the world