Speaker
Mathis Gerdes
(University of Amsterdam)
Description
We explore continuous flows as generative models, focusing on their architectural flexibility in implementing equivariance, and test them on the $φ^4$ theory. Using this setup, we show how a machine-learning approach enables transfer between lattice sizes and allows us to learn for a continuous range of theory parameters at once. Investigating the sample efficiency of training, we find that the expressivity of continuous flows may justify their higher numerical cost due to integration.
Primary authors
Mathis Gerdes
(University of Amsterdam)
Pim de Haan