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

FiGLearn: Filter and Graph Learning using Optimal Transport

In many applications, a dataset can be considered as a set of observed s...
10/29/2020 ∙ by Matthias Minder, et al. ∙ 10 ∙ share

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Multilayer Clustered Graph Learning

Multilayer graphs are appealing mathematical tools for modeling multiple...
10/29/2020 ∙ by Mireille El Gheche, et al. ∙ 66 ∙ share

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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...
10/19/2020 ∙ by Guillermo Ortiz-Jiménez, et al. ∙ 5 ∙ share

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FAR: A General Framework for Attributional Robustness

Attribution maps have gained popularity as tools for explaining neural n...
10/14/2020 ∙ by Adam Ivankay, et al. ∙ 7 ∙ share

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Modurec: Recommender Systems with Feature and Time Modulation

Current state of the art algorithms for recommender systems are mainly b...
10/13/2020 ∙ by Javier Maroto, et al. ∙ 5 ∙ share

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Graph signal processing for machine learning: A review and new perspectives

The effective representation, processing, analysis, and visualization of...
07/31/2020 ∙ by Xiaowen Dong, et al. ∙ 46 ∙ share

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node2coords: Graph Representation Learning with Wasserstein Barycenters

In order to perform network analysis tasks, representations that capture...
07/31/2020 ∙ by Effrosyni Simou, et al. ∙ 0 ∙ share

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Towards robust sensing for Autonomous Vehicles: An adversarial perspective

Autonomous Vehicles rely on accurate and robust sensor observations for ...
07/14/2020 ∙ by Apostolos Modas, et al. ∙ 0 ∙ share

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Building powerful and equivariant graph neural networks with message-passing

Message-passing has proved to be an effective way to design graph neural...
06/26/2020 ∙ by Clément Vignac, et al. ∙ 1 ∙ share

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Graph Pooling with Node Proximity for Hierarchical Representation Learning

Graph neural networks have attracted wide attentions to enable represent...
06/19/2020 ∙ by Xing Gao, et al. ∙ 0 ∙ share

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Neural Anisotropy Directions

In this work, we analyze the role of the network architecture in shaping...
06/17/2020 ∙ by Guillermo Ortiz-Jiménez, et al. ∙ 0 ∙ share

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GeoDA: a geometric framework for black-box adversarial attacks

Adversarial examples are known as carefully perturbed images fooling ima...
03/13/2020 ∙ by Ali Rahmati, et al. ∙ 0 ∙ share

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Wasserstein-based Graph Alignment

We propose a novel method for comparing non-aligned graphs of different ...
03/12/2020 ∙ by Hermina Petric Maretic, et al. ∙ 8 ∙ share

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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...
02/15/2020 ∙ by Guillermo Ortiz-Jiménez, et al. ∙ 4 ∙ share

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Joint Graph-based Depth Refinement and Normal Estimation

Depth estimation is an essential component in understanding the 3D geome...
12/03/2019 ∙ by Mattia Rossi, et al. ∙ 0 ∙ share

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Multi-view shape estimation of transparent containers

The 3D localisation of an object and the estimation of its properties, s...
11/27/2019 ∙ by Alessio Xompero, et al. ∙ 0 ∙ share

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On the choice of graph neural network architectures

Seminal works on graph neural networks have primarily targeted semi-supe...
11/13/2019 ∙ by Clément Vignac, et al. ∙ 80 ∙ share

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Imperceptible Adversarial Attacks on Tabular Data

Security of machine learning models is a concern as they may face advers...
11/08/2019 ∙ by Vincent Ballet, et al. ∙ 0 ∙ share

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Mask Combination of Multi-layer Graphs for Global Structure Inference

Structure inference is an important task for network data processing and...
10/22/2019 ∙ by Eda Bayram, et al. ∙ 10 ∙ share

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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...
09/20/2019 ∙ by Guillermo Ortiz-Jiménez, et al. ∙ 0 ∙ share

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iPool -- Information-based Pooling in Hierarchical Graph Neural Networks

With the advent of data science, the analysis of network or graph data h...
07/01/2019 ∙ by Xing Gao, et al. ∙ 4 ∙ share

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GOT: An Optimal Transport framework for Graph comparison

We present a novel framework based on optimal transport for the challeng...
06/05/2019 ∙ by Hermina Petric Maretic, et al. ∙ 0 ∙ share

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A geometry-inspired decision-based attack

Deep neural networks have recently achieved tremendous success in image ...
03/26/2019 ∙ by Yujia Liu, et al. ∙ 0 ∙ share

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Graph heat mixture model learning

Graph inference methods have recently attracted a great interest from th...
01/24/2019 ∙ by Hermina Petric Maretic, et al. ∙ 0 ∙ share

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Visual Distortions in 360-degree Videos

Omnidirectional (or 360-degree) images and videos are emergent signals i...
01/07/2019 ∙ by Roberto G. de A. Azevedo, et al. ∙ 0 ∙ share

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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...
12/13/2018 ∙ by Mireille El Gheche, et al. ∙ 0 ∙ share

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Robustness via curvature regularization, and vice versa

State-of-the-art classifiers have been shown to be largely vulnerable to...
11/23/2018 ∙ by Seyed-Mohsen Moosavi-Dezfooli, et al. ∙ 0 ∙ share

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Spherical clustering of users navigating 360^∘ content

In Virtual Reality (VR) applications, understanding how users explore th...
11/13/2018 ∙ by Silvia Rossi, et al. ∙ 0 ∙ share

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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 ...
11/06/2018 ∙ by Arun Venkitaraman, et al. ∙ 0 ∙ share

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SparseFool: a few pixels make a big difference

Deep Neural Networks have achieved extraordinary results on image classi...
11/06/2018 ∙ by Apostolos Modas, et al. ∙ 0 ∙ share

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Supervised Linear Regression for Graph Learning from Graph Signals

We propose a supervised learning approach for predicting an underlying g...
11/05/2018 ∙ by Arun Venkitaraman, et al. ∙ 0 ∙ share

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Multilayer Graph Signal Clustering

Multilayer graphs are commonly used to model relationships of different ...
11/02/2018 ∙ by Mireille El Gheche, et al. ∙ 0 ∙ share

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Graph Laplacian mixture model

Graph learning methods have recently been receiving increasing interest ...
10/23/2018 ∙ by Hermina Petric Maretic, et al. ∙ 0 ∙ share

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Isometric Transformation Invariant Graph-based Deep Neural Network

Learning transformation invariant representations of visual data is an i...
08/21/2018 ∙ by Renata Khasanova, et al. ∙ 12 ∙ share

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Learning Graphs from Data: A Signal Representation Perspective

The construction of a meaningful graph topology plays a crucial role in ...
06/03/2018 ∙ by Xiaowen Dong, et al. ∙ 0 ∙ share

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The Sum-Rate-Distortion Region of Correlated Gauss-Markov Sources

We derive the sum-rate-distortion region for a generic number of success...
04/10/2018 ∙ by Giuseppe Cocco, et al. ∙ 0 ∙ share

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Adaptive Streaming in Interactive Multiview Video Systems

Multiview applications endow final users with the possibility to freely ...
02/25/2018 ∙ by Xue Zhang, et al. ∙ 0 ∙ share

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IRSA Transmission Optimization via Online Learning

In this work, we propose a new learning framework for optimising transmi...
01/27/2018 ∙ by Laura Toni, et al. ∙ 0 ∙ share

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Graph-based Transform Coding with Application to Image Compression

In this paper, we propose a new graph-based coding framework and illustr...
12/18/2017 ∙ by Giulia Fracastoro, et al. ∙ 0 ∙ share

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Adaptive Quantization for Deep Neural Network

In recent years Deep Neural Networks (DNNs) have been rapidly developed ...
12/04/2017 ∙ by Yiren Zhou, et al. ∙ 0 ∙ share

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Geometric robustness of deep networks: analysis and improvement

Deep convolutional neural networks have been shown to be vulnerable to a...
11/24/2017 ∙ by Can Kanbak, et al. ∙ 0 ∙ share

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Online Resource Inference in Network Utility Maximization Problems

The amount of transmitted data in computer networks is expected to grow ...
11/20/2017 ∙ by Stefano D'Aronco, et al. ∙ 0 ∙ share

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Graph-Based Classification of Omnidirectional Images

Omnidirectional cameras are widely used in such areas as robotics and vi...
07/26/2017 ∙ by Renata Khasanova, et al. ∙ 0 ∙ share

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Graph learning under sparsity priors

Graph signals offer a very generic and natural representation for data t...
07/18/2017 ∙ by Hermina Petric Maretic, et al. ∙ 0 ∙ share

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Analysis of universal adversarial perturbations

Deep networks have recently been shown to be vulnerable to universal per...
05/26/2017 ∙ by Seyed-Mohsen Moosavi-Dezfooli, et al. ∙ 0 ∙ share

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Classification regions of deep neural networks

The goal of this paper is to analyze the geometric properties of deep ne...
05/26/2017 ∙ by Alhussein Fawzi, et al. ∙ 0 ∙ share

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Optimized Data Representation for Interactive Multiview Navigation

In contrary to traditional media streaming services where a unique media...
05/08/2017 ∙ by Rui Ma, et al. ∙ 0 ∙ share

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Graph-based Isometry Invariant Representation Learning

Learning transformation invariant representations of visual data is an i...
03/01/2017 ∙ by Renata Khasanova, et al. ∙ 0 ∙ share

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Light Field Super-Resolution Via Graph-Based Regularization

Light field cameras capture the 3D information in a scene with a single ...
01/09/2017 ∙ by Mattia Rossi, et al. ∙ 0 ∙ share

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Learning heat diffusion graphs

Effective information analysis generally boils down to properly identify...
11/04/2016 ∙ by Dorina Thanou, et al. ∙ 0 ∙ share

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Pascal Frossard

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