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Convergence analysis of OT-Flow for sample generation
Second, since the loss function will be approximated by Monte Carlo method in training, we established the convergence between the discrete loss function and the continuous one when the sample number $N$ goes to infinity as well.
A deep learning framework for geodesics under spherical Wasserstein-Fisher-Rao metric and its application for weighted sample generation
In this paper, we develop a deep learning framework to compute the geodesics under the spherical WFR metric, and the learned geodesics can be adopted to generate weighted samples.
Unsupervised Learning of Parsimonious General-Purpose Embeddings for User and Location Modelling
Many social network applications depend on robust representations of spatio-temporal data.
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