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UID:2123@i2m.univ-amu.fr
DTSTART;TZID=Europe/Paris:20180119T140000
DTEND;TZID=Europe/Paris:20180119T150000
DTSTAMP:20180104T130000Z
URL:https://www.i2m.univ-amu.fr/evenements/optimal-transport-for-machine-l
 earning/
SUMMARY: (...): Optimal transport for machine learning
DESCRIPTION:: First we present a brief introduction to optimal transport an
 d to the Wasserstein distance. Next we will discuss recent applications of
  OT in Machine Learning. OT can be used to estimate a mapping between dist
 ributions for color adaptation between images and domain adaptation. But i
 t is also a very powerful data fitting term for learning with histograms o
 r empirical distributions for classification\, audio spectral unmixing and
  Generative Adversarial networks.http://remi.flamary.com/index.fr.html
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DTSTART:20171029T020000
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