Publications (216)
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…
Polynomial Filtering for Fast Convergence in Distributed Consensus
Effrosyni Kokiopoulou, Pascal Frossard
In the past few years, the problem of distributed consensus has received a lot of attention, particularly in the framework of ad hoc sensor networks. Most methods proposed in the l…
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…
MiDi: Mixed Graph and 3D Denoising Diffusion for Molecule Generation
Clement Vignac, Nagham Osman, Laura Toni +1
This work introduces MiDi, a novel diffusion model for jointly generating molecular graphs and their corresponding 3D arrangement of atoms. Unlike existing methods that rely on pre…
GOT: An Optimal Transport framework for Graph comparison
Hermina Petric Maretic, Mireille EL Gheche, Giovanni Chierchia +1
We present a novel framework based on optimal transport for the challenging problem of comparing graphs. Specifically, we exploit the probabilistic distribution of smooth graph sig…
On the benefits of knowledge distillation for adversarial robustness
Javier Maroto, Guillermo Ortiz-Jiménez, Pascal Frossard
Knowledge distillation is normally used to compress a big network, or teacher, onto a smaller one, the student, by training it to match its outputs. Recently, some works have shown…
SafeAMC: Adversarial training for robust modulation recognition models
Javier Maroto, Gérôme Bovet, Pascal Frossard
In communication systems, there are many tasks, like modulation recognition, which rely on Deep Neural Networks (DNNs) models. However, these models have been shown to be susceptib…
Multi-task additive models with shared transfer functions based on dictionary learning
Alhussein Fawzi, Mathieu Sinn, Pascal Frossard
Additive models form a widely popular class of regression models which represent the relation between covariates and response variables as the sum of low-dimensional transfer funct…
Optimal Layered Representation for Adaptive Interactive Multiview Video Streaming
Ana De Abreu, Laura Toni, Nikolaos Thomos +3
We consider an interactive multiview video streaming (IMVS) system where clients select their preferred viewpoint in a given navigation window. To provide high quality IMVS, many h…
Multiscale Event Detection in Social Media
Xiaowen Dong, Dimitrios Mavroeidis, Francesco Calabrese +1
Event detection has been one of the most important research topics in social media analysis. Most of the traditional approaches detect events based on fixed temporal and spatial re…
A Classification-Guided Approach for Adversarial Attacks against Neural Machine Translation
Sahar Sadrizadeh, Ljiljana Dolamic, Pascal Frossard
Neural Machine Translation (NMT) models have been shown to be vulnerable to adversarial attacks, wherein carefully crafted perturbations of the input can mislead the target model.…
CLAD: A Contrastive Learning based Approach for Background Debiasing
Ke Wang, Harshitha Machiraju, Oh-Hyeon Choung +2
Convolutional neural networks (CNNs) have achieved superhuman performance in multiple vision tasks, especially image classification. However, unlike humans, CNNs leverage spurious…
Optimized Quantization in Distributed Graph Signal Filtering
Isabela Cunha Maia Nobre, Pascal Frossard
Distributed graph signal processing algorithms require the network nodes to communicate by exchanging messages in order to achieve a common objective. These messages have a finite…
Interactive multiview video system with non-complex navigation at the decoder
Thomas Maugey, Pascal Frossard
Multiview video with interactive and smooth view switching at the receiver is a challenging application with several issues in terms of effective use of storage and bandwidth resou…
Distributional Reduction: Unifying Dimensionality Reduction and Clustering with Gromov-Wasserstein
Hugues Van Assel, Cédric Vincent-Cuaz, Nicolas Courty +3
Unsupervised learning aims to capture the underlying structure of potentially large and high-dimensional datasets. Traditionally, this involves using dimensionality reduction (DR)…
Generating Directed Graphs with Dual Attention and Asymmetric Encoding
Alba Carballo-Castro, Manuel Madeira, Yiming Qin +2
Directed graphs naturally model systems with asymmetric, ordered relationships, essential to applications in biology, transportation, social networks, and visual understanding. Gen…
Hierarchical Training of Deep Neural Networks Using Early Exiting
Yamin Sepehri, Pedram Pad, Ahmet Caner Yüzügüler +2
Deep neural networks provide state-of-the-art accuracy for vision tasks but they require significant resources for training. Thus, they are trained on cloud servers far from the ed…
Markov Decision Process Based Energy-Efficient On-Line Scheduling for Slice-Parallel Video Decoders on Multicore Systems
Nicholas Mastronarde, Karim Kanoun, David Atienza +2
We consider the problem of energy-efficient on-line scheduling for slice-parallel video decoders on multicore systems. We assume that each of the processors are Dynamic Voltage Fre…
Semantic Document Derendering: SVG Reconstruction via Vision-Language Modeling
Adam Hazimeh, Ke Wang, Mark Collier +3
Multimedia documents such as slide presentations and posters are designed to be interactive and easy to modify. Yet, they are often distributed in a static raster format, which lim…
Visual Distortions in 360-degree Videos
Roberto G. de A. Azevedo, Neil Birkbeck, Francesca De Simone +3
Omnidirectional (or 360-degree) images and videos are emergent signals in many areas such as robotics and virtual/augmented reality. In particular, for virtual reality, they allow…
Decoding Delay Minimization in Inter-Session Network Coding
Eirina Bourtsoulatze, Nikolaos Thomos, Pascal Frossard
Intra-session network coding has been shown to offer significant gains in terms of achievable throughput and delay in settings where one source multicasts data to several clients.…
Distributed sensor failure detection in sensor networks
Tamara Tosic, Nikolaos Thomos, Pascal Frossard
We investigate the problem of distributed sensors' failure detection in networks with a small number of defective sensors, whose measurements differ significantly from neighboring…
Robustness of classifiers to universal perturbations: a geometric perspective
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi +2
Deep networks have recently been shown to be vulnerable to universal perturbations: there exist very small image-agnostic perturbations that cause most natural images to be misclas…
Towards robust sensing for Autonomous Vehicles: An adversarial perspective
Apostolos Modas, Ricardo Sanchez-Matilla, Pascal Frossard +1
Autonomous Vehicles rely on accurate and robust sensor observations for safety critical decision-making in a variety of conditions. Fundamental building blocks of such systems are…
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…
DiGress: Discrete Denoising diffusion for graph generation
Clement Vignac, Igor Krawczuk, Antoine Siraudin +3
This work introduces DiGress, a discrete denoising diffusion model for generating graphs with categorical node and edge attributes. Our model utilizes a discrete diffusion process…
RePercENT: Scaling Disentangled Representation Learning Beyond Two Modalities
Vasiliki Rizou, Pascal Frossard, Dorina Thanou
To leverage the full potential of multimodal data, we need representations that go beyond the state-of-the-art alignment and fusion approaches and exploit all cross-modal interacti…
TransFool: An Adversarial Attack against Neural Machine Translation Models
Sahar Sadrizadeh, Ljiljana Dolamic, Pascal Frossard
Deep neural networks have been shown to be vulnerable to small perturbations of their inputs, known as adversarial attacks. In this paper, we investigate the vulnerability of Neura…
Targeted Adversarial Attacks against Neural Machine Translation
Sahar Sadrizadeh, AmirHossein Dabiri Aghdam, Ljiljana Dolamic +1
Neural Machine Translation (NMT) systems are used in various applications. However, it has been shown that they are vulnerable to very small perturbations of their inputs, known as…
Approximate Decoding Approaches for Network Coded Correlated Data
Hyunggon Park, Nikolaos Thomos, Pascal Frossard
This paper considers a framework where data from correlated sources are transmitted with help of network coding in ad-hoc network topologies. The correlated data are encoded indepe…
Clustering with Multi-Layer Graphs: A Spectral Perspective
Xiaowen Dong, Pascal Frossard, Pierre Vandergheynst +1
Observational data usually comes with a multimodal nature, which means that it can be naturally represented by a multi-layer graph whose layers share the same set of vertices (user…
Correlation Estimation from Compressed Images
Vijayaraghavan Thirumalai, Pascal Frossard
This paper addresses the problem of correlation estimation in sets of compressed images. We consider a framework where images are represented under the form of linear measurements…
Task Addition and Weight Disentanglement in Closed-Vocabulary Models
Adam Hazimeh, Alessandro Favero, Pascal Frossard
Task arithmetic has recently emerged as a promising method for editing pre-trained \textit{open-vocabulary} models, offering a cost-effective alternative to standard multi-task fin…
How Compositional Generalization and Creativity Improve as Diffusion Models are Trained
Alessandro Favero, Antonio Sclocchi, Francesco Cagnetta +2
Natural data is often organized as a hierarchical composition of features. How many samples do generative models need in order to learn the composition rules, so as to produce a co…
Neural Anisotropy Directions
Guillermo Ortiz-Jimenez, Apostolos Modas, Seyed-Mohsen Moosavi-Dezfooli +1
In this work, we analyze the role of the network architecture in shaping the inductive bias of deep classifiers. To that end, we start by focusing on a very simple problem, i.e., c…
Deep End-to-End Survival Analysis with Temporal Consistency
Mariana Vargas Vieyra, Pascal Frossard
In this study, we present a novel Survival Analysis algorithm designed to efficiently handle large-scale longitudinal data. Our approach draws inspiration from Reinforcement Learni…
Adaptive Prioritized Random Linear Coding and Scheduling for Layered Data Delivery from Multiple Servers
Nikolaos Thomos, Eymen Kurdoglu, Pascal Frossard +1
In this paper, we deal with the problem of jointly determining the optimal coding strategy and the scheduling decisions when receivers obtain layered data from multiple servers. Th…
Manitest: Are classifiers really invariant?
Alhussein Fawzi, Pascal Frossard
Invariance to geometric transformations is a highly desirable property of automatic classifiers in many image recognition tasks. Nevertheless, it is unclear to which extent state-o…
The Emerging Field of Signal Processing on Graphs: Extending High-Dimensional Data Analysis to Networks and Other Irregular Domains
David I Shuman, Sunil K. Narang, Pascal Frossard +2
In applications such as social, energy, transportation, sensor, and neuronal networks, high-dimensional data naturally reside on the vertices of weighted graphs. The emerging field…
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…
What can linearized neural networks actually say about generalization?
Guillermo Ortiz-Jiménez, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard
For certain infinitely-wide neural networks, the neural tangent kernel (NTK) theory fully characterizes generalization, but for the networks used in practice, the empirical NTK onl…
On the choice of graph neural network architectures
Clément Vignac, Guillermo Ortiz-Jiménez, Pascal Frossard
Seminal works on graph neural networks have primarily targeted semi-supervised node classification problems with few observed labels and high-dimensional signals. With the developm…
Unsupervised Disentanglement Without Compromises : How Functional Orthogonality Enforces Identifiability
Mathieu Cyrille Simon, Pascal Frossard, Christophe De Vleeschouwer
This paper explores unsupervised disentangled representation learning from a functional perspective. We define latent concepts as factors that influence observations through locall…
Backdoor Unlearning by Linear Task Decomposition
Amel Abdelraheem, Alessandro Favero, Gerome Bovet +1
Foundation models have revolutionized computer vision by enabling broad generalization across diverse tasks. Yet, they remain highly susceptible to adversarial perturbations and ta…
Pareto Manifold Learning: Tackling multiple tasks via ensembles of single-task models
Nikolaos Dimitriadis, Pascal Frossard, François Fleuret
In Multi-Task Learning (MTL), tasks may compete and limit the performance achieved on each other, rather than guiding the optimization to a solution, superior to all its single-tas…
A Structured Dictionary Perspective on Implicit Neural Representations
Gizem Yüce, Guillermo Ortiz-Jiménez, Beril Besbinar +1
Implicit neural representations (INRs) have recently emerged as a promising alternative to classical discretized representations of signals. Nevertheless, despite their practical s…
A note on the data-driven capacity of P2P networks
Jacob Chakareski, Pascal Frossard, Hervé Kerivin +2
We consider two capacity problems in P2P networks. In the first one, the nodes have an infinite amount of data to send and the goal is to optimally allocate their uplink bandwidths…
FGOT: Graph Distances based on Filters and Optimal Transport
Hermina Petric Maretic, Mireille El Gheche, Giovanni Chierchia +1
Graph comparison deals with identifying similarities and dissimilarities between graphs. A major obstacle is the unknown alignment of graphs, as well as the lack of accurate and in…
Learning Smooth Pattern Transformation Manifolds
Elif Vural, Pascal Frossard
Manifold models provide low-dimensional representations that are useful for processing and analyzing data in a transformation-invariant way. In this paper, we study the problem of…
Subgraph Matching via Partial Optimal Transport
Wen-Xin Pan, Isabel Haasler, Pascal Frossard
In this work, we propose a novel approach for subgraph matching, the problem of finding a given query graph in a large source graph, based on the fused Gromov-Wasserstein distance.…
QUACKIE: A NLP Classification Task With Ground Truth Explanations
Yves Rychener, Xavier Renard, Djamé Seddah +2
NLP Interpretability aims to increase trust in model predictions. This makes evaluating interpretability approaches a pressing issue. There are multiple datasets for evaluating NLP…
Task Arithmetic in the Tangent Space: Improved Editing of Pre-Trained Models
Guillermo Ortiz-Jimenez, Alessandro Favero, Pascal Frossard
Task arithmetic has recently emerged as a cost-effective and scalable approach to edit pre-trained models directly in weight space: By adding the fine-tuned weights of different ta…
Graph Transform Optimization with Application to Image Compression
Giulia Fracastoro, Dorina Thanou, Pascal Frossard
In this paper, we propose a new graph-based transform and illustrate its potential application to signal compression. Our approach relies on the careful design of a graph that opti…
Thresholding-based reconstruction of compressed correlated signals
Alhussein Fawzi, Tamara Tosic, Pascal Frossard
We consider the problem of recovering a set of correlated signals (e.g., images from different viewpoints) from a few linear measurements per signal. We assume that each sensor in…
Privacy-Preserving Image Acquisition Using Trainable Optical Kernel
Yamin Sepehri, Pedram Pad, Pascal Frossard +1
Preserving privacy is a growing concern in our society where sensors and cameras are ubiquitous. In this work, for the first time, we propose a trainable image acquisition method t…
Graph-based classification of multiple observation sets
Effrosyni Kokiopoulou, Pascal Frossard
We consider the problem of classification of an object given multiple observations that possibly include different transformations. The possible transformations of the object gener…
Fixed-Point Masked Generative Modeling
Andrea Miele, Yiming Qin, Alba Carballo-Castro +2
Masked Generative Models (MGMs) enable parallel decoding and achieve strong performance across modalities, but require full-sequence bidirectional transformers at every step, makin…
GeoDA: a geometric framework for black-box adversarial attacks
Ali Rahmati, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard +1
Adversarial examples are known as carefully perturbed images fooling image classifiers. We propose a geometric framework to generate adversarial examples in one of the most challen…
On the Granularity of Explanations in Model Agnostic NLP Interpretability
Yves Rychener, Xavier Renard, Djamé Seddah +2
Current methods for Black-Box NLP interpretability, like LIME or SHAP, are based on altering the text to interpret by removing words and modeling the Black-Box response. In this pa…
Dictionary learning for fast classification based on soft-thresholding
Alhussein Fawzi, Mike Davies, Pascal Frossard
Classifiers based on sparse representations have recently been shown to provide excellent results in many visual recognition and classification tasks. However, the high cost of com…
Catastrophic overfitting can be induced with discriminative non-robust features
Guillermo Ortiz-Jiménez, Pau de Jorge, Amartya Sanyal +5
Adversarial training (AT) is the de facto method for building robust neural networks, but it can be computationally expensive. To mitigate this, fast single-step attacks can be use…
Traffic signal prediction on transportation networks using spatio-temporal correlations on graphs
Semin Kwak, Nikolas Geroliminis, Pascal Frossard
Multivariate time series forecasting poses challenges as the variables are intertwined in time and space, like in the case of traffic signals. Defining signals on graphs relaxes su…
Tangent space estimation for smooth embeddings of Riemannian manifolds
Hemant Tyagi, Elif Vural, Pascal Frossard
Numerous dimensionality reduction problems in data analysis involve the recovery of low-dimensional models or the learning of manifolds underlying sets of data. Many manifold learn…
Data augmentation with mixtures of max-entropy transformations for filling-level classification
Apostolos Modas, Andrea Cavallaro, Pascal Frossard
We address the problem of distribution shifts in test-time data with a principled data augmentation scheme for the task of content-level classification. In such a task, properties…
Graph-based representation for multiview image coding
Thomas Maugey, Antonio Ortega, Pascal Frossard
In this paper, we propose a new representation for multiview image sets. Our approach relies on graphs to describe geometry information in a compact and controllable way. The links…
Message Passing in Graph Convolution Networks via Adaptive Filter Banks
Xing Gao, Wenrui Dai, Chenglin Li +3
Graph convolution networks, like message passing graph convolution networks (MPGCNs), have been a powerful tool in representation learning of networked data. However, when data is…
Forward-Backward Splitting for Optimal Transport based Problems
Guillermo Ortiz-Jimenez, Mireille El Gheche, Effrosyni Simou +2
Optimal transport aims to estimate a transportation plan that minimizes a displacement cost. This is realized by optimizing the scalar product between the sought plan and the given…
Robustness of classifiers: from adversarial to random noise
Alhussein Fawzi, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard
Several recent works have shown that state-of-the-art classifiers are vulnerable to worst-case (i.e., adversarial) perturbations of the datapoints. On the other hand, it has been e…
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…
Classification regions of deep neural networks
Alhussein Fawzi, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard +1
The goal of this paper is to analyze the geometric properties of deep neural network classifiers in the input space. We specifically study the topology of classification regions cr…
Graph-based Isometry Invariant Representation Learning
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…
DiPhon: Diffusion on Graphons for Scalable Graph Generation
Sergio Rozada, Yiming Qin, Manuel Madeira +2
Diffusion models represent a leading paradigm for graph generation, with notable impact in domains such as molecular design. Yet, scaling these models to large graphs remains an op…
Prioritized Random MAC Optimization via Graph-based Analysis
Laura Toni, Pascal Frossard
Motivated by the analogy between successive interference cancellation and iterative belief-propagation on erasure channels, irregular repetition slotted ALOHA (IRSA) strategies hav…
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…
Multi-view shape estimation of transparent containers
Alessio Xompero, Ricardo Sanchez-Matilla, Apostolos Modas +2
The 3D localisation of an object and the estimation of its properties, such as shape and dimensions, are challenging under varying degrees of transparency and lighting conditions.…
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…
Geodesics of Dynamic Graphs for Regime Change Detection
William Cappelletti, Ãtienne Voutaz, Pascal Frossard
Traditional change point detection in dynamic networks assumes abrupt transitions between stationary states, overlooking scenarios of continuous evolution which arise in most real-…
Multi-modal image retrieval with random walk on multi-layer graphs
Renata Khasanova, Xiaowen Dong, Pascal Frossard
The analysis of large collections of image data is still a challenging problem due to the difficulty of capturing the true concepts in visual data. The similarity between images co…
Bio-inspired Robustness: A Review
Harshitha Machiraju, Oh-Hyeon Choung, Pascal Frossard +1
Deep convolutional neural networks (DCNNs) have revolutionized computer vision and are often advocated as good models of the human visual system. However, there are currently many…
Joint Reconstruction of Multi-view Compressed Images
Vijayaraghavan Thirumalai, Pascal Frossard
The distributed representation of correlated multi-view images is an important problem that arise in vision sensor networks. This paper concentrates on the joint reconstruction pro…
Optimized Packet Scheduling in Multiview Video Navigation Systems
Laura Toni, Thomas Maugey, Pascal Frossard
In multiview video systems, multiple cameras generally acquire the same scene from different perspectives, such that users have the possibility to select their preferred viewpoint.…
A Relaxed Optimization Approach for Adversarial Attacks against Neural Machine Translation Models
Sahar Sadrizadeh, Clément Barbier, Ljiljana Dolamic +1
In this paper, we propose an optimization-based adversarial attack against Neural Machine Translation (NMT) models. First, we propose an optimization problem to generate adversaria…
Convolutional neural networks on irregular domains based on approximate vertex-domain translations
Bastien Pasdeloup, Vincent Gripon, Jean-Charles Vialatte +2
We propose a generalization of convolutional neural networks (CNNs) to irregular domains, through the use of a translation operator on a graph structure. In regular settings such a…
ODySSeI: An Open-Source End-to-End Framework for Automated Detection, Segmentation, and Severity Estimation of Lesions in Invasive Coronary Angiography Images
Anand Choudhary, Xiaowu Sun, Thabo Mahendiran +8
Invasive Coronary Angiography (ICA) is the clinical gold standard for the assessment of coronary artery disease. However, its interpretation remains subjective and prone to intra-…
DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models
Yiming Qin, Kai Yi, Miruna Cretu +3
Designing small molecule ligands that bind with high affinity to specific protein pockets is a fundamental goal in drug discovery, as small molecules constitute a major fraction of…
IS-Fusion: Instance-Scene Collaborative Fusion for Multimodal 3D Object Detection
Junbo Yin, Jianbing Shen, Runnan Chen +4
Bird's eye view (BEV) representation has emerged as a dominant solution for describing 3D space in autonomous driving scenarios. However, objects in the BEV representation typicall…
A Fast Volumetric Capture and Reconstruction Pipeline for Dynamic Point Clouds and Gaussian Splats
Athanasios Charisoudis, Simone Croci, Lam Kit Yung +2
We present a fast and efficient volumetric capture and reconstruction system that processes either RGB-D or RGB-only input to generate 3D representations in the form of point cloud…
Analysis of classifiers' robustness to adversarial perturbations
Alhussein Fawzi, Omar Fawzi, Pascal Frossard
The goal of this paper is to analyze an intriguing phenomenon recently discovered in deep networks, namely their instability to adversarial perturbations (Szegedy et. al., 2014). W…
FiGLearn: Filter and Graph Learning using Optimal Transport
Matthias Minder, Zahra Farsijani, Dhruti Shah +2
In many applications, a dataset can be considered as a set of observed signals that live on an unknown underlying graph structure. Some of these signals may be seen as white noise…
Intermediate Performance Analysis of Growth Codes
Nikolaos Thomos, Rethnakaran Pulikkoonattu, Pascal Frossard
Growth codes are a subclass of Rateless codes that have found interesting applications in data dissemination problems. Compared to other Rateless and conventional channel codes, Gr…
node2coords: Graph Representation Learning with Wasserstein Barycenters
Effrosyni Simou, Dorina Thanou, Pascal Frossard
In order to perform network analysis tasks, representations that capture the most relevant information in the graph structure are needed. However, existing methods do not learn rep…
Online Network Source Optimization with Graph-Kernel MAB
Laura Toni, Pascal Frossard
We propose Grab-UCB, a graph-kernel multi-arms bandit algorithm to learn online the optimal source placement in large scale networks, such that the reward obtained from a priori un…
Distributed Rate Allocation in Inter-Session Network Coding
Eirina Bourtsoulatze, Nikolaos Thomos, Pascal Frossard
In this work, we propose a distributed rate allocation algorithm that minimizes the average decoding delay for multimedia clients in inter-session network coding systems. We consid…
Multi-feature 360 Video Quality Estimation
Roberto G. de A. Azevedo, Neil Birkbeck, Ivan Janatra +2
We propose a new method for the visual quality assessment of 360-degree (omnidirectional) videos. The proposed method is based on computing multiple spatio-temporal objective quali…
Optimized Adaptive Streaming Representations based on System Dynamics
Laura Toni, Ramon Aparicio-Pardo, Karine Pires +3
Adaptive streaming addresses the increasing and heterogenous demand of multimedia content over the Internet by offering several encoded versions for each video sequence. Each versi…
Multi-View Video Packet Scheduling
Laura Toni, Thomas Maugey, Pascal Frossard
In multiview applications, multiple cameras acquire the same scene from different viewpoints and generally produce correlated video streams. This results in large amounts of highly…
Maximum Likelihood Distillation for Robust Modulation Classification
Javier Maroto, Gérôme Bovet, Pascal Frossard
Deep Neural Networks are being extensively used in communication systems and Automatic Modulation Classification (AMC) in particular. However, they are very susceptible to small ad…
Self-Supervision by Prediction for Object Discovery in Videos
Beril Besbinar, Pascal Frossard
Despite their irresistible success, deep learning algorithms still heavily rely on annotated data. On the other hand, unsupervised settings pose many challenges, especially about d…
Graph Learning Should Move Beyond Restrictive Views of Spectral and Message-Passing GNNs
Antonis Vasileiou, Juan Cervino, Pascal Frossard +7
Graph neural networks (GNNs) are commonly divided into message-passing neural networks (MPNNs) and spectral GNNs, reflecting two largely separate research traditions in machine lea…
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…