fast_wasserstein_revised_final2.dvi

Source URL: marcocuturi.net

Clustering  and  Model  Integration  under  the   Wasserstein  Metric Jia Li Department  of  Statistics Penn  State  University

Source URL: adapt.psu.edu

Wasserstein Generative Adversarial Networks Martin Arjovsky 1 Soumith Chintala 2 L´eon Bottou 1 2 Abstract We introduce a new algorithm named WGAN, an alternative to traditional GAN training. In

Source URL: proceedings.mlr.press

Ricci curvature of Markov chains on metric spaces Yann Ollivier Abstract We define the coarse Ricci curvature of metric spaces in terms of how much small balls are closer (in Wasserstein transportation distance) than

Source URL: www.yann-ollivier.org

Wasserstein Propagation for Semi-Supervised Learning Justin Solomon JUSTIN . SOLOMON @ STANFORD . EDU Raif M. Rustamov RUSTAMOV @ STANFORD . EDU

Source URL: jmlr.org

GEOMETRIC APPLICATIONS OF WASSERSTEIN DISTANCE SZYMON M. WALCZAK ´ Z, ´ POLAND UNIVERSITY OF LOD

Source URL: faculty.ms.u-tokyo.ac.jp

Contractions in Wasserstein distance for Markov semigroup, a dimensional approach I. Gentil Institut Camille Jordan, Lyon 1 Lancaster

Source URL: www.lancaster.ac.uk

Clare G Wickman* (). Probabilistic Frames: Ideas from Optimal Transport. Preliminary report. Abstract: Probabilistic frames are subset of the Wasserstein space of probability measures

Source URL: jointmathematicsmeetings.org

Convolutional Wasserstein Distances: Efficient Optimal Transportation on Geometric Domains Justin Solomon Stanford University Adrian Butscher Autodesk, Inc.

Source URL: www.iip.ist.i.kyoto-u.ac.jp

• Measure theory
• Algebraic topology
• Algebraic geometry
• Field theory
• Valuation
• Kullback–Leibler divergence
• Wasserstein metric
• Trigonometric functions
• Transportation theory
• Mathematical analysis
• Mathematics
• Abstract algebra