4.6k citations · 7.9k across the 144 of their papers we have counts for
13 papers · 1 filter
Graph Signal Representation with Wasserstein Barycenters
Effrosyni Simou, Pascal Frossard
In many applications signals reside on the vertices of weighted graphs. Thus, there is the need to learn low dimensional representations for graph signals that will allow for data…
Stochastic Gradient Descent for Spectral Embedding with Implicit Orthogonality Constraint
Mireille El Gheche, Giovanni Chierchia, Pascal Frossard
In this paper, we propose a scalable algorithm for spectral embedding. The latter is a standard tool for graph clustering. However, its computational bottleneck is the eigendecompo…
Robustness via curvature regularization, and vice versa
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Jonathan Uesato +1
State-of-the-art classifiers have been shown to be largely vulnerable to adversarial perturbations. One of the most effective strategies to improve robustness is adversarial traini…
Kernel Regression for Graph Signal Prediction in Presence of Sparse Noise
Arun Venkitaraman, Pascal Frossard, Saikat Chatterjee
In presence of sparse noise we propose kernel regression for predicting output vectors which are smooth over a given graph. Sparse noise models the training outputs being corrupted…
Supervised Linear Regression for Graph Learning from Graph Signals
Arun Venkitaraman, Hermina Petric Maretic, Saikat Chatterjee +1
We propose a supervised learning approach for predicting an underlying graph from a set of graph signals. Our approach is based on linear regression. In the linear regression model…
Spherical clustering of users navigating 360° content
Silvia Rossi, Francesca De Simone, Pascal Frossard +1
In Virtual Reality (VR) applications, understanding how users explore the omnidirectional content is important to optimize content creation, to develop user-centric services, or ev…
SparseFool: a few pixels make a big difference
Apostolos Modas, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard
Deep Neural Networks have achieved extraordinary results on image classification tasks, but have been shown to be vulnerable to attacks with carefully crafted perturbations of the…
OrthoNet: Multilayer Network Data Clustering
Mireille El Gheche, Giovanni Chierchia, Pascal Frossard
Network data appears in very diverse applications, like biological, social, or sensor networks. Clustering of network nodes into categories or communities has thus become a very co…
Graph Laplacian mixture model
Hermina Petric Maretic, Pascal Frossard
Graph learning methods have recently been receiving increasing interest as means to infer structure in datasets. Most of the recent approaches focus on different relationships betw…
Isometric Transformation Invariant Graph-based Deep Neural Network
Renata Khasanova, Pascal Frossard
Learning transformation invariant representations of visual data is an important problem in computer vision. Deep convolutional networks have demonstrated remarkable results for im…
Learning graphs from data: A signal representation perspective
Xiaowen Dong, Dorina Thanou, Michael Rabbat +1
The construction of a meaningful graph topology plays a crucial role in the effective representation, processing, analysis and visualization of structured data. When a natural choi…
Adaptive Streaming in Interactive Multiview Video Systems
Xue Zhang, Laura Toni, Pascal Frossard +2
Multiview applications endow final users with the possibility to freely navigate within 3D scenes with minimum-delay. A real feeling of scene navigation is enabled by transmitting…
IRSA Transmission Optimization via Online Learning
Laura Toni, Pascal Frossard
In this work, we propose a new learning framework for optimising transmission strategies when irregular repetition slotted ALOHA (IRSA) MAC protocol is considered. We cast the onli…