In this episode, i’m joined by Marco Cuturi, professor of statistics at Université Paris-Saclay.
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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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Mentioned in the Interview
- CREST – Center for Research in Economics and Statistics
- ENSAE Paris
- Université Paris-Saclay
- Optimal Transport
- Optimal Transport Project Github
- Optimal Transport & Machine Learning
- ‘Antifragile,’ by Nassim Nicholas Taleb
- Kullback-Leibler Divergence
- Cross Entropy
- Word Movers Distance
- Wasserstein GAN
- Register for the AI Summit
- Check out @ShirinGlander’s Great TWIML Sketches!
- TWIML Presents: Series page
- TWIML Events Page
- TWIML Meetup
- TWIML Newsletter
“More On That Later” by Lee Rosevere licensed under CC By 4.0