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Pascal Frossard

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Publications associées (428)

Sparse Attacks for Manipulating Explanations in Deep Neural Network Models

Pascal Frossard, Seyed Mohsen Moosavi Dezfooli, Ahmad Ajalloeian, Michail Vlachos

We investigate methods for manipulating classifier explanations while keeping the predictions unchanged. Our focus is on using a sparse attack, which seeks to alter only a minimal number of input features. We present an efficient and novel algorithm for co ...

Ieee Computer Soc2023

MiDi: Mixed Graph and 3D Denoising Diffusion for Molecule Generation

Pascal Frossard

This work introduces MiDi, a novel diffusion model for jointly generating molecular graphs and their corresponding 3D atom arrangements. Unlike existing methods that rely on predefined rules to determine molecular bonds based on the 3D conformation, MiDi o ...

Springer International Publishing Ag2023

Knowledge Distillation with Graph Neural Networks for Epileptic Seizure Detection

Pascal Frossard, Arun Venkitaraman

Wearable devices for seizure monitoring detection could significantly improve the quality of life of epileptic patients. However, existing solutions that mostly rely on full electrode set of electroencephalogram (EEG) measurements could be inconvenient for ...

Springer International Publishing Ag2023

Quality-Constrained Encoding Optimization for Omnidirectional Video Streaming

Pascal Frossard, Roberto Gerson De Albuquerque Azevedo, Chaofan He

Omnidirectional video streaming is usually implemented based on the representations of tiles, where the tiles are obtained by splitting the video frame into several rectangular areas and each tile is converted into multiple representations with different r ...

Ieee-Inst Electrical Electronics Engineers Inc2023

DiGress: Discrete Denoising diffusion for graph generation

Pascal Frossard, Volkan Cevher, Igor Krawczuk, Clément Arthur Yvon Vignac, Bohan Wang

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 that progressively edits graphs with noise, through the process of adding or ...

2023

Deep learning-based prediction of future myocardial infarction using invasive coronary angiography: a feasibility study

Pascal Frossard, Emmanuel Abbé, Ntorina Thanou, Omar Raita, Ortal Yona Senouf, Thabodhan Mahendiran

BackgroundAngiographic parameters can facilitate the risk stratification of coronary lesions but remain insufficient in the prediction of future myocardial infarction (MI).AIMSWe compared the ability of humans, angiographic parameters and deep learning (DL ...

BMJ PUBLISHING GROUP2023

Scale-out Systolic Arrays

Pascal Frossard, Babak Falsafi, Ahmet Caner Yüzügüler

Multi-pod systolic arrays are emerging as the architecture of choice in DNN inference accelerators. Despite their potential, designing multi-pod systolic arrays to maximize effective throughput/Watt-i.e., throughput/Watt adjusted when accounting for array ...

ASSOC COMPUTING MACHINERY2023

Distributed Graph Learning With Smooth Data Priors

Pascal Frossard, Isabela Cunha Maia Nobre, Mireille El Gheche

Graph learning is often a necessary step in processing or representing structured data, when the underlying graph is not given explicitly. Graph learning is generally performed centrally with a full knowledge of the graph signals, namely the data that live ...

IEEE2022

SafeAMC: Adversarial training for robust modulation classification models

Pascal Frossard, Javier Alejandro Maroto Morales

In communication systems, there are many tasks, like modulation classification, for which Deep Neural Networks (DNNs) have obtained promising performance. However, these models have been shown to be susceptible to adversarial perturbations, namely impercep ...

IEEE2022

fGOT: Graph Distances Based on Filters and Optimal Transport

Pascal Frossard, Giovanni Chierchia, Hermina Petric Maretic, Mireille El Gheche

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 inexpensive comparison metrics. In this work we introduce the filter graph dis ...

ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE2022

Interpretable temporal-spatial graph attention network for multi-site PV power forecasting

Pascal Frossard, Baptiste Schubnel, Rafael Eduardo Carrillo Rangel, Pierre-Jean Alet, Jelena Simeunovic

Accurate forecasting of photovoltaic (PV) and wind production is crucial for the integration of more renewable energy sources into the power grid. To address the limited resolution and costs of methods based on numerical weather predictions (NWP), we take ...

ELSEVIER SCI LTD2022

Landmarking for Navigational Streaming of Stored High-Dimensional Media

Pascal Frossard, Yuan Yuan

Modern media data such as 360 degrees videos and light field (LF) images are typically captured in much higher dimensions than the observers' visual displays. To efficiently browse high-dimensional media, a navigational streaming model is considered: a cli ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

OSLO: On-the-Sphere Learning for Omnidirectional Images and Its Application to 360-Degree Image Compression

Pascal Frossard, Thomas Maugey, Roberto Gerson De Albuquerque Azevedo

State-of-the-art 2D image compression schemes rely on the power of convolutional neural networks (CNNs). Although CNNs offer promising perspectives for 2D image compression, extending such models to omnidirectional images is not straightforward. First, omn ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

U-Boost NAS: Utilization-Boosted Differentiable Neural Architecture Search

Pascal Frossard, Ahmet Caner Yüzügüler, Nikolaos Dimitriadis

Optimizing resource utilization in target platforms is key to achieving high performance during DNN inference. While optimizations have been proposed for inference latency, memory footprint, and energy consumption, prior hardware-aware neural architecture ...

SPRINGER INTERNATIONAL PUBLISHING AG2022

Graph Neural Networks With Lifting-Based Adaptive Graph Wavelets

Pascal Frossard, Chenglin Li, Mingxing Xu

Spectral-based graph neural networks (SGNNs) have been attracting increasing attention in graph representation learning. However, existing SGNNs are limited in implementing graph filters with rigid transforms and cannot adapt to signals residing on graphs ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Wasserstein-Based Graph Alignment

Pascal Frossard, Hermina Petric Maretic, Mireille El Gheche

A novel method for comparing non-aligned graphs of various sizes is proposed, based on the Wasserstein distance between graph signal distributions induced by the respective graph Laplacian matrices. Specifically, a new formulation for the one-to-many graph ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Attention-based learning of views fusion applied to myocardial infarction diagnosis from x-ray CT

Pascal Frossard, Emmanuel Abbé, Stéphane Fournier, Ntorina Thanou, David Meier, Jakub Jan Gwizdala, Ortal Yona Senouf, Olivier Muller

2022

Adversarial training with informed data selection

Pascal Frossard, Javier Alejandro Maroto Morales, Marcele Oliveira Kuhfuss de Mendonça

With the increasing amount of available data and advances in computing capabilities, deep neural networks (DNNs) have been successfully employed to solve challenging tasks in various areas, including healthcare, climate, and finance. Nevertheless, state-of ...

IEEE2022

A Structured Dictionary Perspective on Implicit Neural Representations

Pascal Frossard, Beril Besbinar, Guillermo Ortiz Jimenez, Gizem Yüce

Implicit neural representations (INRs) have recently emerged as a promising alternative to classical discretized representations of signals. Nevertheless, despite their practical success, we still do not understand how INRs represent signals. We propose a ...

IEEE COMPUTER SOC2022

Dynamics of functional network organization through graph mixture learning

Pascal Frossard, Dimitri Nestor Alice Van De Ville, Hermina Petric Maretic, Anjali Bagunu Tarun, Ilaria Ricchi

Understanding the organizational principles of human brain activity at the systems level remains a major challenge in network neuroscience. Here, we introduce a fully data-driven approach based on graph learning to extract meaningful repeating network patt ...

ACADEMIC PRESS INC ELSEVIER SCIENCE2022

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