activity
20202024
most citedOn the benefits of knowledge distillation for adversarial robustness

10 citations · 19 across the 5 of their papers we have counts for

collaborators

7 papers

cs.LG2024

PUMA: margin-based data pruning

Javier Maroto, Pascal Frossard

Deep learning has been able to outperform humans in terms of classification accuracy in many tasks. However, to achieve robustness to adversarial perturbations, the best methodolog…

cs.LG2023

Adversarial training with informed data selection

Marcele O. K. Mendonça, Javier Maroto, Pascal Frossard +1

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 vario…

cs.LG2022

Maximum Likelihood Distillation for Robust Modulation Classification

Javier Maroto, Gérôme Bovet, Pascal Frossard

Deep Neural Networks are being extensively used in communication systems and Automatic Modulation Classification (AMC) in particular. However, they are very susceptible to small ad…

cs.LG2022★ 10 cited

On the benefits of knowledge distillation for adversarial robustness

Javier Maroto, Guillermo Ortiz-Jiménez, Pascal Frossard

Knowledge distillation is normally used to compress a big network, or teacher, onto a smaller one, the student, by training it to match its outputs. Recently, some works have shown…

eess.SP2021★ 9 cited

SafeAMC: Adversarial training for robust modulation recognition models

Javier Maroto, Gérôme Bovet, Pascal Frossard

In communication systems, there are many tasks, like modulation recognition, which rely on Deep Neural Networks (DNNs) models. However, these models have been shown to be susceptib…

eess.SP2021

On the benefits of robust models in modulation recognition

Javier Maroto, Gérôme Bovet, Pascal Frossard

Given the rapid changes in telecommunication systems and their higher dependence on artificial intelligence, it is increasingly important to have models that can perform well under…

cs.IR2020

Modurec: Recommender Systems with Feature and Time Modulation

Javier Maroto, Clément Vignac, Pascal Frossard

Current state of the art algorithms for recommender systems are mainly based on collaborative filtering, which exploits user ratings to discover latent factors in the data. These a…