Robust normalizing flows using Bernstein-type polynomials

We propose a framework to construct (Kobyzev et al., 2020). NFs based on increasing triangular maps and Bernstein-type polynomials. Compared to the In contrast, normalizing flows (NFs) are a category of generative existing (universal) NF frameworks, our method models that enable exact density computation and provides compelling advantages like theoretical efficient sampling. Since the seminal work by Rezende upper bounds for the approximation error, robustness, & Mohamed (2015), NFs have been gaining increasing attention higher interpretability, suitability for compactly from the machine learning community due to the supported densities, and the ability to employ attractive properties mentioned earlier. In the abstract, NFs higher degree polynomials without training consist of a series diffeomorphisms that transforms a simple instability. Moreover, we provide a constructive distribution into a more complex one, which in turn universality proof, which gives analytic expressions allows an analytical density estimation of samples. In the of the approximations for known transformations.