Today, network operators still lack functional network models able to make accurate predictions of end-to-end Key Performance Indicators (e.g., delay).This thesis introduces the benchmark for computer network modeling using ...

Transformer Networks are a new type of Deep Learning architecture first introduced in 2017. By only applying attention mechanisms, the transformer network can model relations between text sequences that outperformed other ...

In this work we present a tool that generates real world political networks from user provided lists of politicians and news sites. We use as input a dataset of current Texas politicians and 6 news sites to illustrate the ...

Many socio-economic studies are nowadays trying to accomplish a complete description of how the different elements of our society and world are connected. This work is an attempt to build an architecture that provides an ...

Since Wide and Deep Learning for Recommender Systems appeared in 2016, multiple architecture models have been created around this idea of jointly train a wide and deep neural networks as this architecture allow the model ...

An exploratory and predictive analysis on GPS data is presented. Physical performance variables from professional football players are analysed in a holistic approach that involves data exploration, analysis of adaptation ...