Testing the boundaries: Normalizing Flows for higher dimensional data sets
This work addresses the challenge of applying Normalizing Flows to complex, high-dimensional data in fields like High Energy Physics, but it appears incremental as it focuses on testing existing methods without introducing new techniques.
The paper investigates the robustness of Normalizing Flows as generative models for high-dimensional data, specifically testing their performance on toy datasets with increasing dimensions, but does not report concrete numerical results.
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.