
Improving filling level classification with adversarial training
We investigate the problem of classifying  from a single image  the le...
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QUACKIE: A NLP Classification Task With Ground Truth Explanations
NLP Interpretability aims to increase trust in model predictions. This m...
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SentenceBased Model Agnostic NLP Interpretability
Today, interpretability of BlackBox Natural Language Processing (NLP) m...
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FiGLearn: Filter and Graph Learning using Optimal Transport
In many applications, a dataset can be considered as a set of observed s...
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Multilayer Clustered Graph Learning
Multilayer graphs are appealing mathematical tools for modeling multiple...
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Optimism in the Face of Adversity: Understanding and Improving Deep Learning through Adversarial Robustness
Driven by massive amounts of data and important advances in computationa...
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FAR: A General Framework for Attributional Robustness
Attribution maps have gained popularity as tools for explaining neural n...
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Modurec: Recommender Systems with Feature and Time Modulation
Current state of the art algorithms for recommender systems are mainly b...
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Graph signal processing for machine learning: A review and new perspectives
The effective representation, processing, analysis, and visualization of...
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node2coords: Graph Representation Learning with Wasserstein Barycenters
In order to perform network analysis tasks, representations that capture...
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Towards robust sensing for Autonomous Vehicles: An adversarial perspective
Autonomous Vehicles rely on accurate and robust sensor observations for ...
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Building powerful and equivariant graph neural networks with messagepassing
Messagepassing has proved to be an effective way to design graph neural...
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Graph Pooling with Node Proximity for Hierarchical Representation Learning
Graph neural networks have attracted wide attentions to enable represent...
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Neural Anisotropy Directions
In this work, we analyze the role of the network architecture in shaping...
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GeoDA: a geometric framework for blackbox adversarial attacks
Adversarial examples are known as carefully perturbed images fooling ima...
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Wassersteinbased Graph Alignment
We propose a novel method for comparing nonaligned graphs of different ...
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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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Joint Graphbased Depth Refinement and Normal Estimation
Depth estimation is an essential component in understanding the 3D geome...
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Multiview shape estimation of transparent containers
The 3D localisation of an object and the estimation of its properties, s...
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On the choice of graph neural network architectures
Seminal works on graph neural networks have primarily targeted semisupe...
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Imperceptible Adversarial Attacks on Tabular Data
Security of machine learning models is a concern as they may face advers...
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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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CDOT: Continuous Domain Adaptation using Optimal Transport
In this work, we address the scenario in which the target domain is cont...
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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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GOT: An Optimal Transport framework for Graph comparison
We present a novel framework based on optimal transport for the challeng...
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A geometryinspired decisionbased attack
Deep neural networks have recently achieved tremendous success in image ...
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Graph heat mixture model learning
Graph inference methods have recently attracted a great interest from th...
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Visual Distortions in 360degree Videos
Omnidirectional (or 360degree) images and videos are emergent signals i...
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Stochastic Gradient Descent for Spectral Embedding with Implicit Orthogonality Constraint
In this paper, we propose a scalable algorithm for spectral embedding. T...
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Robustness via curvature regularization, and vice versa
Stateoftheart classifiers have been shown to be largely vulnerable to...
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Spherical clustering of users navigating 360^∘ content
In Virtual Reality (VR) applications, understanding how users explore th...
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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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SparseFool: a few pixels make a big difference
Deep Neural Networks have achieved extraordinary results on image classi...
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Supervised Linear Regression for Graph Learning from Graph Signals
We propose a supervised learning approach for predicting an underlying g...
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Multilayer Graph Signal Clustering
Multilayer graphs are commonly used to model relationships of different ...
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Graph Laplacian mixture model
Graph learning methods have recently been receiving increasing interest ...
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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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Learning Graphs from Data: A Signal Representation Perspective
The construction of a meaningful graph topology plays a crucial role in ...
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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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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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Graphbased Transform Coding with Application to Image Compression
In this paper, we propose a new graphbased coding framework and illustr...
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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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Geometric robustness of deep networks: analysis and improvement
Deep convolutional neural networks have been shown to be vulnerable to a...
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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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GraphBased Classification of Omnidirectional Images
Omnidirectional cameras are widely used in such areas as robotics and vi...
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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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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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Optimized Data Representation for Interactive Multiview Navigation
In contrary to traditional media streaming services where a unique media...
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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