Fast Sinkhorn I: An O (N) algorithm for the Wasserstein-1 metric
The Wasserstein metric is broadly used in optimal transport for comparing two probabilistic
distributions, with successful applications in various fields such as machine learning, signal …
distributions, with successful applications in various fields such as machine learning, signal …
Fast Sinkhorn II: Collinear triangular matrix and linear time accurate computation of optimal transport
In our previous work (Liao et al. in Commun Math Sci, 2022), the complexity of Sinkhorn
iteration is reduced from O (N 2) to the optimal O (N) by leveraging the special structure of …
iteration is reduced from O (N 2) to the optimal O (N) by leveraging the special structure of …
An Unsupervised Deep Learning Approach for the Wave Equation Inverse Problem
Full-waveform inversion (FWI) is a powerful geophysical imaging technique that infers high-
resolution subsurface physical parameters by solving a non-convex optimization problem …
resolution subsurface physical parameters by solving a non-convex optimization problem …
A numerical algorithm with linear complexity for Multi-marginal Optimal Transport with Cost
Numerically solving multi-marginal optimal transport (MMOT) problems is computationally
prohibitive, even for moderate-scale instances involving $ l\ge4 $ marginals with support …
prohibitive, even for moderate-scale instances involving $ l\ge4 $ marginals with support …
[图书][B] Computational Inversion with Wasserstein Distances and Neural Network Induced Loss Functions
W Ding - 2022 - search.proquest.com
This thesis presents a systematic computational investigation of loss functions in solving
inverse problems of partial differential equations. The primary efforts are spent on …
inverse problems of partial differential equations. The primary efforts are spent on …