One of the core challenges in scientific computing is to have access to probability distributions which are too complex to be manipulated. The traditional answer to this computational issue lies in the Monte Carlo methods, which provide a way of approximating the moments of target probability distributions by sampling them, while avoiding the expensive numerical integration originally required. Despite the extremely significant contribution these methodologies have brought in computational science, there are still problems for which their performance is not efficient enough. This is the case of high-dimensional, multi-modal probability distributions. An interesting way to deal with this challenging task is to assist Monte Carlo sampling with machine learning techniques, such as the generative models, which are proved to be efficient in sampling complex high-dimensional probability distributions.