Normalizing Flow Maps

Normalizing Flow Maps learn invertible transports between arbitrary pairs of times while retaining exact density evaluation. This combines few-step generation with likelihoods and sampling in both temporal directions. The talk develops pointwise and distributional consistency objectives, including forward and reverse KL formulations, connecting flow-map regression with likelihood training. Image-generation experiments and equilibrium sampling of small peptides test the approach against earlier normalizing-flow methods.