Optimal transport for machine learning
Date(s) : 19/01/2018 iCal
14h00 - 15h00
First we present a brief introduction to optimal transport and to the Wasserstein distance. Next we will discuss recent applications of OT in Machine Learning. OT can be used to estimate a mapping between distributions for color adaptation between images and domain adaptation. But it is also a very powerful data fitting term for learning with histograms or empirical distributions for classification, audio spectral unmixing and Generative Adversarial networks.
http://remi.flamary.com/index.fr.html
Catégories Pas de Catégories
