BriefGPT.xyz
Jun, 2019
利用分割 - 瓦磨斯坦距离学习生成模型的渐进保证
Asymptotic Guarantees for Learning Generative Models with the Sliced-Wasserstein Distance
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Kimia Nadjahi, Alain Durmus, Umut Şimşekli, Roland Badeau
TL;DR
本研究探讨了通过最小化切片-瓦瑟斯坦距离获得的估计量的渐近性质,证明了其一致性和中心极限定理。
Abstract
minimum expected distance estimation
(MEDE) algorithms have been widely used for
probabilistic models
with intractable likelihood functions and they have become increasingly popular due to their use in implicit <
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