Normalizing Flows (NFs) are emerging as a powerful class of generative models, as they not only allow for efficient sampling, but also deliver, by construction, density estimation. They are of great potential usage in High Energy Physics (HEP), where complex high dimensional data and probability distributions are everyday's meal. However, in order to fully leverage the potential of NFs it is crucial to explore their robustness as data dimensionality increases. Thus, in this contribution, we discuss the performances of some of the most popular types of NFs on the market, on some toy data sets with increasing number of dimensions.

翻译：正常化流动(NFs)正在成为一个强大的基因模型类别,因为它们不仅能够进行高效取样,而且能够以建筑方式提供密度估计,在高能物理(HEP)极有可能使用,因为复杂的高维数据和概率分布是日常的膳食。然而,为了充分利用NFs的潜力,随着数据维度的提高,必须探索其坚固性。因此,在这一贡献中,我们讨论了市场上一些最受欢迎的NFs的性能,以及一些数量不断增加的玩具数据集的性能。