Pierre Vandergheynst

research

∙ 04/06/2023

Interpretable statistical representations of neural population dynamics and geometry

The dynamics of neuron populations during diverse tasks often evolve on ...

0 Adam Gosztolai, et al. ∙

research

∙ 05/31/2022

Generalised Implicit Neural Representations

We consider the problem of learning implicit neural representations (INR...

19 Daniele Grattarola, et al. ∙

research

∙ 02/17/2020

What is Trending on Wikipedia? Capturing Trends and Language Biases Across Wikipedia Editions

In this work, we propose an automatic evaluation and comparison of the b...

0 Volodymyr Miz, et al. ∙

research

∙ 05/23/2019

Some limitations of norm based generalization bounds in deep neural networks

Deep convolutional neural networks have been shown to be able to fit a l...

5 Konstantinos Pitas, et al. ∙

research

∙ 05/21/2019

Revisiting hard thresholding for DNN pruning

The most common method for DNN pruning is hard thresholding of network w...

4 Konstantinos Pitas, et al. ∙

research

∙ 03/20/2019

A Graph-structured Dataset for Wikipedia Research

Wikipedia is a rich and invaluable source of information. Its central pl...

0 Nicolas Aspert, et al. ∙

research

∙ 03/12/2018

FeTa: A DCA Pruning Algorithm with Generalization Error Guarantees

Recent DNN pruning algorithms have succeeded in reducing the number of p...

0 Konstantinos Pitas, et al. ∙

research

∙ 02/21/2018

Spectrally approximating large graphs with smaller graphs

How does coarsening affect the spectrum of a general graph? We provide c...

0 Andreas Loukas, et al. ∙

research

∙ 02/16/2018

Joint Estimation of Room Geometry and Modes with Compressed Sensing

Acoustical behavior of a room for a given position of microphone and sou...

0 Helena Peic Tukuljac, et al. ∙

research

∙ 12/30/2017

PAC-Bayesian Margin Bounds for Convolutional Neural Networks - Technical Report

Recently the generalisation error of deep neural networks has been analy...

0 Pitas Konstantinos, et al. ∙

research

∙ 10/01/2017

Wikipedia graph mining: dynamic structure of collective memory

Wikipedia is the biggest encyclopedia ever created and the fifth most vi...

0 Volodymyr Miz, et al. ∙

research

∙ 07/14/2017

Localization of Sound Sources in a Room with One Microphone

Estimation of the location of sound sources is usually done using microp...

0 Helena Peic Tukuljac, et al. ∙

research

∙ 06/12/2017

Fast Approximate Spectral Clustering for Dynamic Networks

Spectral clustering is a widely studied problem, yet its complexity is p...

0 Lionel Martin, et al. ∙

research

∙ 02/19/2017

Compressive Embedding and Visualization using Graphs

Visualizing high-dimensional data has been a focus in data analysis comm...

0 Johan Paratte, et al. ∙

research

∙ 12/22/2016

Structured Sequence Modeling with Graph Convolutional Recurrent Networks

This paper introduces Graph Convolutional Recurrent Network (GCRN), a de...

0 Youngjoo Seo, et al. ∙

research

∙ 11/24/2016

Geometric deep learning: going beyond Euclidean data

Many scientific fields study data with an underlying structure that is a...

0 Michael M. Bronstein, et al. ∙

research

∙ 11/15/2016

Multilinear Low-Rank Tensors on Graphs & Applications

We propose a new framework for the analysis of low-rank tensors which li...

0 Nauman Shahid, et al. ∙

research

∙ 10/04/2016

Adaptive Graph-based Total Variation for Tomographic Reconstructions

Sparsity exploiting image reconstruction (SER) methods have been extensi...

0 Faisal, et al. ∙

research

∙ 06/30/2016

Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering

In this work, we are interested in generalizing convolutional neural net...

0 Michaël Defferrard, et al. ∙

research

∙ 06/22/2016

Towards stationary time-vertex signal processing

Graph-based methods for signal processing have shown promise for the ana...

0 Nathanaël Perraudin, et al. ∙

research

∙ 03/14/2016

Graph Based Sinogram Denoising for Tomographic Reconstructions

Limited data and low dose constraints are common problems in a variety o...

0 Faisal, et al. ∙

research

∙ 03/10/2016

Global and Local Uncertainty Principles for Signals on Graphs

Uncertainty principles such as Heisenberg's provide limits on the time-f...

0 Nathanaël Perraudin, et al. ∙

research

∙ 02/05/2016

Compressive Spectral Clustering

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

0 Nicolas Tremblay, et al. ∙

research

∙ 01/11/2016

Stationary signal processing on graphs

Graphs are a central tool in machine learning and information processing...

0 Nathanaël Perraudin, et al. ∙

research

∙ 01/08/2016

Song Recommendation with Non-Negative Matrix Factorization and Graph Total Variation

This work formulates a novel song recommender system as a matrix complet...

0 Kirell Benzi, et al. ∙

research

∙ 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. ∙

research

∙ 11/16/2015

Graph-based denoising for time-varying point clouds

Noisy 3D point clouds arise in many applications. They may be due to err...

0 Yann Schoenenberger, et al. ∙

research

∙ 07/29/2015

Fast Robust PCA on Graphs

Mining useful clusters from high dimensional data has received significa...

0 Nauman Shahid, et al. ∙

research

∙ 04/23/2015

Robust Principal Component Analysis on Graphs

Principal Component Analysis (PCA) is the most widely used tool for line...

0 Nauman Shahid, et al. ∙

research

∙ 01/26/2015

Geodesic convolutional neural networks on Riemannian manifolds

Feature descriptors play a crucial role in a wide range of geometry anal...

0 Jonathan Masci, et al. ∙

research

∙ 12/27/2014

Functional correspondence by matrix completion

In this paper, we consider the problem of finding dense intrinsic corres...

0 Artiom Kovnatsky, et al. ∙

research

∙ 08/07/2014

Matrix Completion on Graphs

The problem of finding the missing values of a matrix given a few of its...

0 Vassilis Kalofolias, et al. ∙

research

∙ 06/30/2014

Learning Laplacian Matrix in Smooth Graph Signal Representations

The construction of a meaningful graph plays a crucial role in the succe...

0 Xiaowen Dong, et al. ∙

research

∙ 02/04/2014

UNLocBoX: A MATLAB convex optimization toolbox for proximal-splitting methods

Convex optimization is an essential tool for machine learning, as many o...

0 Nathanaël Perraudin, et al. ∙

research

∙ 03/09/2013

Clustering on Multi-Layer Graphs via Subspace Analysis on Grassmann Manifolds

Relationships between entities in datasets are often of multiple nature,...

0 Xiaowen Dong, et al. ∙

research

∙ 12/13/2012

Robust image reconstruction from multi-view measurements

We propose a novel method to accurately reconstruct a set of images repr...

0 Gilles Puy, et al. ∙

research

∙ 11/06/2012

From Bits to Images: Inversion of Local Binary Descriptors

Local Binary Descriptors are becoming more and more popular for image ma...

0 Emmanuel d'Angelo, et al. ∙

research

∙ 06/11/2011

Clustering with Multi-Layer Graphs: A Spectral Perspective

Observational data usually comes with a multimodal nature, which means t...

0 Xiaowen Dong, et al. ∙

research

∙ 05/10/2010

Classification via Incoherent Subspaces

This article presents a new classification framework that can extract in...

0 Karin Schnass, et al. ∙

Pierre Vandergheynst

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