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pro vyhledávání: '"Tanguy, Eloi"'
We investigate finding a map $g$ within a function class $G$ that minimises an Optimal Transport (OT) cost between a target measure $\nu$ and the image by $g$ of a source measure $\mu$. This is relevant when an OT map from $\mu$ to $\nu$ does not exi
Externí odkaz:
http://arxiv.org/abs/2407.13445
Autor:
Tanguy, Eloi
Publikováno v:
Transactions on Machine Learning Research, 2023 2835-8856
Optimal Transport has sparked vivid interest in recent years, in particular thanks to the Wasserstein distance, which provides a geometrically sensible and intuitive way of comparing probability measures. For computational reasons, the Sliced Wassers
Externí odkaz:
http://arxiv.org/abs/2307.11714
Publikováno v:
Mathematics of Computation (2024)
The Sliced Wasserstein (SW) distance has become a popular alternative to the Wasserstein distance for comparing probability measures. Widespread applications include image processing, domain adaptation and generative modelling, where it is common to
Externí odkaz:
http://arxiv.org/abs/2307.10352
This paper deals with the reconstruction of a discrete measure $\gamma_Z$ on $\mathbb{R}^d$ from the knowledge of its pushforward measures $P_i\#\gamma_Z$ by linear applications $P_i: \mathbb{R}^d \rightarrow \mathbb{R}^{d_i}$ (for instance projectio
Externí odkaz:
http://arxiv.org/abs/2304.12029
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