10 citations · 19 across the 3 of their papers we have counts for
5 papers
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…
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…
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…
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…
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…