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Found 5 papers, 2 papers with code
Bridging the Gap Between Variational Inference and Wasserstein Gradient Flows
Variational inference is a technique that approximates a target distribution by optimizing within the parameter space of variational families.
Minimizing $f$-Divergences by Interpolating Velocity Fields
To perform such movements we need to calculate the corresponding velocity fields which include a density ratio function between these two distributions.
MonoFlow: Rethinking Divergence GANs via the Perspective of Wasserstein Gradient Flows
The conventional understanding of adversarial training in generative adversarial networks (GANs) is that the discriminator is trained to estimate a divergence, and the generator learns to minimize this divergence.
Sliced Wasserstein Variational Inference
For example, it is not a proper metric, i. e., it is non-symmetric and does not preserve the triangle inequality.
Posterior Ratio Estimation of Latent Variables
Density Ratio Estimation has attracted attention from the machine learning community due to its ability to compare the underlying distributions of two datasets.
