Optimal Transport and Machine Learning with Marco Cuturi

800 800 The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

In this episode, i’m joined by Marco Cuturi, professor of statistics at Université Paris-Saclay.

Marco and I spent some time discussing his work on Optimal Transport Theory at NIPS last year. In our discussion, Marco explains Optimal Transport, which provides a way for us to compare probability measures. We look at ways Optimal Transport can be used across machine learning applications, including graphical, NLP, and image examples. We also touch on GANs, or generative adversarial networks, and some of the challenges they present to the research community.

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About Marco

Mentioned in the Interview

“More On That Later” by Lee Rosevere licensed under CC By 4.0

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