1 citations · 1 across the 3 of their papers we have counts for
3 papers
Wasserstein Flow Matching: Generative modeling over families of distributions
Doron Haviv, Aram-Alexandre Pooladian, Dana Pe'er +1
Generative modeling typically concerns transporting a single source distribution to a target distribution via simple probability flows. However, in fields like computer graphics an…
Wasserstein Wormhole: Scalable Optimal Transport Distance with Transformers
Doron Haviv, Russell Zhang Kunes, Thomas Dougherty +4
Optimal transport (OT) and the related Wasserstein metric (W) are powerful and ubiquitous tools for comparing distributions. However, computing pairwise Wasserstein distances rapid…
Gradient Estimation for Binary Latent Variables via Gradient Variance Clipping
Russell Z. Kunes, Mingzhang Yin, Max Land +3
Gradient estimation is often necessary for fitting generative models with discrete latent variables, in contexts such as reinforcement learning and variational autoencoder (VAE) tr…