Search Results for author:
Found 3 papers, 1 papers with code
Fast and Unified Path Gradient Estimators for Normalizing Flows
Recent work shows that path gradient estimators for normalizing flows have lower variance compared to standard estimators for variational inference, resulting in improved training.
Gradients should stay on Path: Better Estimators of the Reverse- and Forward KL Divergence for Normalizing Flows
We propose an algorithm to estimate the path-gradient of both the reverse and forward Kullback-Leibler divergence for an arbitrary manifestly invertible normalizing flow.
Path-Gradient Estimators for Continuous Normalizing Flows
Recent work has established a path-gradient estimator for simple variational Gaussian distributions and has argued that the path-gradient is particularly beneficial in the regime in which the variational distribution approaches the exact target distribution.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
