Optimal transport for machine learning

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Date(s) - 19/01/2018
14 h 00 min - 15 h 00 min

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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.


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