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

Improving filling level classification with adversarial training

We investigate the problem of classifying - from a single image - the le...
02/08/2021 ∙ by Apostolos Modas, et al. ∙ 8 ∙ share

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QUACKIE: A NLP Classification Task With Ground Truth Explanations

NLP Interpretability aims to increase trust in model predictions. This m...
12/24/2020 ∙ by Yves Rychener, et al. ∙ 4 ∙ share

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Sentence-Based Model Agnostic NLP Interpretability

Today, interpretability of Black-Box Natural Language Processing (NLP) m...
12/24/2020 ∙ by Yves Rychener, et al. ∙ 10 ∙ share

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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...
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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Pascal Frossard

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