no code implementations • 6 Nov 2023 • Mohammadhadi Shateri, Francisco Messina, Fabrice Labeau, Pablo Piantanida
In the present work, the overfitting in GANs is studied in terms of the discriminator, and a more general measure of overfitting based on the Bhattacharyya coefficient is defined.
1 code implementation • 30 Jun 2022 • Federica Granese, Marine Picot, Marco Romanelli, Francisco Messina, Pablo Piantanida
Detection of adversarial examples has been a hot topic in the last years due to its importance for safely deploying machine learning algorithms in critical applications.
no code implementations • 19 Aug 2021 • Pablo Gill Estevez, Pablo Marchi, Francisco Messina, Cecilia Galarza
In this paper we develop a systematic methodology for frequency identification and component filtering of non-stationary power system forced oscillations (FO) based on multi-channel TFR.
no code implementations • 17 Jul 2021 • Mohammadhadi Shateri, Francisco Messina, Pablo Piantanida, Fabrice Labeau
We formulate this as the problem of learning a sparse representation of SMs data with minimum information leakage and maximum utility.
1 code implementation • 12 Jun 2021 • Marine Picot, Francisco Messina, Malik Boudiaf, Fabrice Labeau, Ismail Ben Ayed, Pablo Piantanida
Adversarial robustness has become a topic of growing interest in machine learning since it was observed that neural networks tend to be brittle.
no code implementations • 20 Nov 2020 • Mohammadhadi Shateri, Francisco Messina, Pablo Piantanida, Fabrice Labeau
In this paper, we study this problem in the context of time series data and smart meters (SMs) power consumption measurements in particular.
no code implementations • 29 Jun 2020 • Mohammadhadi Shateri, Francisco Messina, Pablo Piantanida, Fabrice Labeau
On the one hand, the releaser in the CAL method, by getting supervision from the actual values of the private variables and feedback from the adversary performance, tries to minimize the adversary log-likelihood.
no code implementations • 10 Jun 2020 • Mohammadhadi Shateri, Francisco Messina, Pablo Piantanida, Fabrice Labeau
Unlike previous studies, we model the whole temporal correlation in the data to learn the MI in its general form and use a neural network to estimate the MI-based reward signal to guide the PCMU learning process.
no code implementations • 10 Mar 2020 • Mohammadhadi Shateri, Francisco Messina, Pablo Piantanida, Fabrice Labeau
Smart meters (SMs) play a pivotal rule in the smart grid by being able to report the electricity usage of consumers to the utility provider (UP) almost in real-time.
no code implementations • 14 Jun 2019 • Mohammadhadi Shateri, Francisco Messina, Pablo Piantanida, Fabrice Labeau
In this paper, we focus on real-time privacy threats, i. e., potential attackers that try to infer sensitive information from SMs data in an online fashion.