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Award Letter Of Lateness

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Preview of Optimizing Functionals on the Space of Probabilities with Input Convex Neural Networks
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Optimizing Functionals on the Space of Probabilities with Input Convex Neural Networks (2106.00774.pdf)

5.64 MBEN 32 pages
Gradient flows are used to optimize functionals in probability spaces with the Wasserstein metric. A typical approach involves the Jordan-Kinderlehrer-Otto scheme, but this is challenging in high dimensions. We propose using input-convex neural networks to approximate the scheme.
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Popular Keywords

functionals input convex neural networks scheme optimizing space probabilities gradient flows optimize probability spaces wasserstein metric typical approach involves jordan kinderlehrer challenging dimensions propose approximate

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