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Wasserstein Wormhole: Scalable Optimal Transport Distance with Transformers
April 16, 2024, 4:42 a.m. | Doron Haviv, Russell Zhang Kunes, Thomas Dougherty, Cassandra Burdziak, Tal Nawy, Anna Gilbert, Dana Pe'er
cs.LG updates on arXiv.org arxiv.org
Abstract: Optimal transport (OT) and the related Wasserstein metric (W) are powerful and ubiquitous tools for comparing distributions. However, computing pairwise Wasserstein distances rapidly becomes intractable as cohort size grows. An attractive alternative would be to find an embedding space in which pairwise Euclidean distances map to OT distances, akin to standard multidimensional scaling (MDS). We present Wasserstein Wormhole, a transformer-based autoencoder that embeds empirical distributions into a latent space wherein Euclidean distances approximate OT distances. …
abstract arxiv computing cs.cg cs.lg embedding however map q-bio.gn scalable space tools transformers transport type
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