340 citations · 396 across the 5 of their papers we have counts for
7 papers
Dynamics of Fourier Modes in Torus Generative Adversarial Networks
Ángel González-Prieto, Alberto Mozo, Edgar Talavera +1
Generative Adversarial Networks (GANs) are powerful Machine Learning models capable of generating fully synthetic samples of a desired phenomenon with a high resolution. Despite th…
Data Augmentation techniques in time series domain: A survey and taxonomy
Guillermo Iglesias, Edgar Talavera, Ángel González-Prieto +2
With the latest advances in Deep Learning-based generative models, it has not taken long to take advantage of their remarkable performance in the area of time series. Deep neural n…
Improving the quality of generative models through Smirnov transformation
Ángel González-Prieto, Alberto Mozo, Sandra Gómez-Canaval +1
Solving the convergence issues of Generative Adversarial Networks (GANs) is one of the most outstanding problems in generative models. In this work, we propose a novel activation f…
Synthetic flow-based cryptomining attack generation through Generative Adversarial Networks
Alberto Mozo, Ángel González-Prieto, Antonio Pastor +2
Due to the growing rise of cyber attacks in the Internet, flow-based data sets are crucial to increase the performance of the Machine Learning (ML) components that run in network-b…
Regularized Greedy Column Subset Selection
Bruno Ordozgoiti, Alberto Mozo, Jesús García López de Lacalle
The Column Subset Selection Problem provides a natural framework for unsupervised feature selection. Despite being a hard combinatorial optimization problem, there exist efficient…
Using Machine Learning to Detect Noisy Neighbors in 5G Networks
Udi Margolin, Alberto Mozo, Bruno Ordozgoiti +3
5G networks are expected to be more dynamic and chaotic in their structure than current networks. With the advent of Network Function Virtualization (NFV), Network Functions (NF) w…
Node Sampling using Random Centrifugal Walks
Andrés Sevilla, Alberto Mozo, Antonio Fernández Anta
Sampling a network with a given probability distribution has been identified as a useful operation. In this paper we propose distributed algorithms for sampling networks, so that n…