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Found 12 papers, 2 papers with code
Understanding deep neural networks through the lens of their non-linearity
The remarkable success of deep neural networks (DNN) is often attributed to their high expressive power and their ability to approximate functions of arbitrary complexity.
Learning representations that are closed-form Monge mapping optimal with application to domain adaptation
Optimal transport (OT) is a powerful geometric tool used to compare and align probability measures following the least effort principle.
Affine Transport for Sim-to-Real Domain Adaptation
In this paper, we present affine transport -- a variant of optimal transport, which models the mapping between state transition distributions between the source and target domains with an affine transformation.
Estimating 2-Sinkhorn Divergence between Gaussian Processes from Finite-Dimensional Marginals
\emph{Optimal Transport} (OT) has emerged as an important computational tool in machine learning and computer vision, providing a geometrical framework for studying probability measures.
Bayesian Inference for Optimal Transport with Stochastic Cost
In machine learning and computer vision, optimal transport has had significant success in learning generative models and defining metric distances between structured and stochastic data objects, that can be cast as probability measures.
Entropy-Regularized $2$-Wasserstein Distance between Gaussian Measures
As the geometries change by varying the regularization magnitude, we study the limiting cases of vanishing and infinite magnitudes, reconfirming well-known results on the limits of the Sinkhorn divergence.
How Well Do WGANs Estimate the Wasserstein Metric?
Generative modelling is often cast as minimizing a similarity measure between a data distribution and a model distribution.
A Formalization of The Natural Gradient Method for General Similarity Measures
The method uses the Kullback-Leibler divergence, corresponding infinitesimally to the Fisher-Rao metric, which is pulled back to the parameter space of a family of probability distributions.
(q,p)-Wasserstein GANs: Comparing Ground Metrics for Wasserstein GANs
We contribute to the WGAN literature by introducing the family of $(q, p)$-Wasserstein GANs, which allow the use of more general $p$-Wasserstein metrics for $p\geq 1$ in the GAN learning procedure.
Wrapped Gaussian Process Regression on Riemannian Manifolds
Gaussian process (GP) regression is a powerful tool in non-parametric regression providing uncertainty estimates.
Probabilistic Riemannian submanifold learning with wrapped Gaussian process latent variable models
Latent variable models (LVMs) learn probabilistic models of data manifolds lying in an \emph{ambient} Euclidean space.
Learning from uncertain curves: The 2-Wasserstein metric for Gaussian processes
We prove uniqueness of the barycenter of a population of GPs, as well as convergence of the metric and the barycenter of their finite-dimensional counterparts.
