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Wasserstein gradient flows are continuous time dynamics that define curves of steepest descent to minimize an objective function over the space of probability measures (i.e., the Wasserstein space).
Brève communication. régularisation d’inéquations variationnelles par approximations successives
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Maximum mean discrepancy gradient flow
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Alain Durmus, Szymon Majewski, and Blazej Miasojedow · 2019
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Gradient descent algorithms for Bures-Wasserstein barycenters
Sinho Chewi, Tyler Maunu, Philippe Rigollet, and Austin J Stromme · 2020
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Approximate inference with Wasserstein gradient flows
Charlie Frogner and Tomaso Poggio · 2020
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