
On the choice of graph neural network architectures
Seminal works on graph neural networks have primarily targeted semisupe...
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Isometric Transformation Invariant Graphbased Deep Neural Network
Learning transformation invariant representations of visual data is an i...
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Mask Combination of Multilayer Graphs for Global Structure Inference
Structure inference is an important task for network data processing and...
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Wassersteinbased Graph Alignment
We propose a novel method for comparing nonaligned graphs of different ...
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iPool  Informationbased Pooling in Hierarchical Graph Neural Networks
With the advent of data science, the analysis of network or graph data h...
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Hold me tight! Influence of discriminative features on deep network boundaries
Important insights towards the explainability of neural networks and the...
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Geometric robustness of deep networks: analysis and improvement
Deep convolutional neural networks have been shown to be vulnerable to a...
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Graph learning under sparsity priors
Graph signals offer a very generic and natural representation for data t...
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GraphBased Classification of Omnidirectional Images
Omnidirectional cameras are widely used in such areas as robotics and vi...
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Analysis of universal adversarial perturbations
Deep networks have recently been shown to be vulnerable to universal per...
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Classification regions of deep neural networks
The goal of this paper is to analyze the geometric properties of deep ne...
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Learning heat diffusion graphs
Effective information analysis generally boils down to properly identify...
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Graphbased Isometry Invariant Representation Learning
Learning transformation invariant representations of visual data is an i...
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Light Field SuperResolution Via GraphBased Regularization
Light field cameras capture the 3D information in a scene with a single ...
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Universal adversarial perturbations
Given a stateoftheart deep neural network classifier, we show the exi...
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Robustness of classifiers: from adversarial to random noise
Several recent works have shown that stateoftheart classifiers are vu...
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Multimodal image retrieval with random walk on multilayer graphs
The analysis of large collections of image data is still a challenging p...
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Multitask additive models with shared transfer functions based on dictionary learning
Additive models form a widely popular class of regression models which r...
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Learning Laplacian Matrix in Smooth Graph Signal Representations
The construction of a meaningful graph plays a crucial role in the succe...
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Multiscale Event Detection in Social Media
Event detection has been one of the most important research topics in so...
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Learning parametric dictionaries for graph signals
In sparse signal representation, the choice of a dictionary often involv...
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Manitest: Are classifiers really invariant?
Invariance to geometric transformations is a highly desirable property o...
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Clustering on MultiLayer Graphs via Subspace Analysis on Grassmann Manifolds
Relationships between entities in datasets are often of multiple nature,...
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Analysis of classifiers' robustness to adversarial perturbations
The goal of this paper is to analyze an intriguing phenomenon recently d...
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Dictionary learning for fast classification based on softthresholding
Classifiers based on sparse representations have recently been shown to ...
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A Study of Image Analysis with Tangent Distance
The computation of the geometric transformation between a reference and ...
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Image registration with sparse approximations in parametric dictionaries
We examine in this paper the problem of image registration from the new ...
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RateDistortion Analysis of Multiview Coding in a DIBR Framework
Depth image based rendering techniques for multiview applications have b...
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Tangentbased manifold approximation with locally linear models
In this paper, we consider the problem of manifold approximation with af...
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Navigation domain representation for interactive multiview imaging
Enabling users to interactively navigate through different viewpoints of...
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Joint Reconstruction of Multiview Compressed Images
The distributed representation of correlated multiview images is an imp...
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Learning Smooth Pattern Transformation Manifolds
Manifold models provide lowdimensional representations that are useful ...
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Discretization of Parametrizable Signal Manifolds
Transformationinvariant analysis of signals often requires the computat...
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Distributed Representation of Geometrically Correlated Images with Compressed Linear Measurements
This paper addresses the problem of distributed coding of images whose c...
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Correlation Estimation from Compressed Images
This paper addresses the problem of correlation estimation in sets of co...
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Clustering with MultiLayer Graphs: A Spectral Perspective
Observational data usually comes with a multimodal nature, which means t...
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Graphbased classification of multiple observation sets
We consider the problem of classification of an object given multiple ob...
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Graphbased compression of dynamic 3D point cloud sequences
This paper addresses the problem of compression of 3D point cloud sequen...
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Adaptive Quantization for Deep Neural Network
In recent years Deep Neural Networks (DNNs) have been rapidly developed ...
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Graphbased Transform Coding with Application to Image Compression
In this paper, we propose a new graphbased coding framework and illustr...
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Online Resource Inference in Network Utility Maximization Problems
The amount of transmitted data in computer networks is expected to grow ...
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Adaptive Streaming in Interactive Multiview Video Systems
Multiview applications endow final users with the possibility to freely ...
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IRSA Transmission Optimization via Online Learning
In this work, we propose a new learning framework for optimising transmi...
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Optimized Data Representation for Interactive Multiview Navigation
In contrary to traditional media streaming services where a unique media...
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The SumRateDistortion Region of Correlated GaussMarkov Sources
We derive the sumratedistortion region for a generic number of success...
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Learning Graphs from Data: A Signal Representation Perspective
The construction of a meaningful graph topology plays a crucial role in ...
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Graph Laplacian mixture model
Graph learning methods have recently been receiving increasing interest ...
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Multilayer Graph Signal Clustering
Multilayer graphs are commonly used to model relationships of different ...
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SparseFool: a few pixels make a big difference
Deep Neural Networks have achieved extraordinary results on image classi...
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Kernel Regression for Graph Signal Prediction in Presence of Sparse Noise
In presence of sparse noise we propose kernel regression for predicting ...
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Pascal Frossard
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Associate Professor in the Electrical Engineering Institute at EPFL, and the Associate Dean for Research in the School of Engineering