17 citations · 34 across the 10 of their papers we have counts for
6 papers · 1 filter
From Alexnet to Transformers: Measuring the Non-linearity of Deep Neural Networks with Affine Optimal Transport
Quentin Bouniot, Ievgen Redko, Anton Mallasto +6
In the last decade, we have witnessed the introduction of several novel deep neural network (DNN) architectures exhibiting ever-increasing performance across diverse tasks. Explain…
Learning representations that are closed-form Monge mapping optimal with application to domain adaptation
Oliver Struckmeier, Ievgen Redko, Anton Mallasto +3
Optimal transport (OT) is a powerful geometric tool used to compare and align probability measures following the least effort principle. Despite its widespread use in machine learn…
Estimating 2-Sinkhorn Divergence between Gaussian Processes from Finite-Dimensional Marginals
Anton Mallasto
\emph{Optimal Transport} (OT) has emerged as an important computational tool in machine learning and computer vision, providing a geometrical framework for studying probability mea…
Bayesian Inference for Optimal Transport with Stochastic Cost
Anton Mallasto, Markus Heinonen, Samuel Kaski
In machine learning and computer vision, optimal transport has had significant success in learning generative models and defining metric distances between structured and stochastic…
How Well Do WGANs Estimate the Wasserstein Metric?
Anton Mallasto, Guido Montúfar, Augusto Gerolin
Generative modelling is often cast as minimizing a similarity measure between a data distribution and a model distribution. Recently, a popular choice for the similarity measure ha…
(q,p)-Wasserstein GANs: Comparing Ground Metrics for Wasserstein GANs
Anton Mallasto, Jes Frellsen, Wouter Boomsma +1
Generative Adversial Networks (GANs) have made a major impact in computer vision and machine learning as generative models. Wasserstein GANs (WGANs) brought Optimal Transport (OT)…