Taming hyperparameter tuning in continuous normalizing flows using the JKO scheme

ABSTRACT: A normalizing flow (NF) is a mapping that transforms a chosen probability distribution to a normal distribution. Such flows are a common technique used for data generation and density estimation in machine learning and data science. The density estimate obtained with a NF requires a change of variables formula that involves the computation of the Jacobian determinant of the NF transformation. In order to tractably compute this determinant, continuous normalizing flows (CNF) estimate the mapping and its Jacobian determinant using a neural ODE. Optimal transport (OT) theory has been successfully used to assist in finding CNFs by formulating them as OT problems with a soft penalty for enforcing the standard normal distribution as a target measure. A drawback of OT-based CNFs is the addition o

SUBMITTER: Vidal A 

PROVIDER: S-EPMC10024737 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature