论文标题
测试边界：将较高维数据集的流量归一化
Testing the boundaries: Normalizing Flows for higher dimensional data sets
论文作者
论文摘要
标准化流（NFS）正成为强大的生成模型类别，因为它们不仅允许进行有效的采样，而且还可以通过构造密度估计来提供。它们在高能量物理（HEP）中具有很大的潜在用途，其中复杂的高维数据和概率分布是每天的餐点。但是，为了充分利用NFS的潜力，随着数据维度的增加，探索其鲁棒性至关重要。因此，在这项贡献中，我们讨论了市场上一些最受欢迎的NFS类型的表现，这些玩具数据集的尺寸越来越多。
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.
