4 citations · 9 across the 10 of their papers we have counts for
8 papers · 1 filter
Electric Currents for Discrete Data Generation
Alexander Kolesov, Stepan Manukhov, Vladimir V. Palyulin +1
We propose lectric urrent iscrete ata eneration (ECDG), a pioneering method for data generation in discrete settin…
Interaction Field Matching: Overcoming Limitations of Electrostatic Models
Stepan I. Manukhov, Alexander Kolesov, Vladimir V. Palyulin +1
Electrostatic field matching (EFM) has recently appeared as a novel physics-inspired paradigm for data generation and transfer using the idea of an electric capacitor. However, it…
Field Matching: an Electrostatic Paradigm to Generate and Transfer Data
Alexander Kolesov, Manukhov Stepan, Vladimir V. Palyulin +1
We propose Electrostatic Field Matching (EFM), a novel method that is suitable for both generative modeling and distribution transfer tasks. Our approach is inspired by the physics…
Estimating Barycenters of Distributions with Neural Optimal Transport
Alexander Kolesov, Petr Mokrov, Igor Udovichenko +4
Given a collection of probability measures, a practitioner sometimes needs to find an "average" distribution which adequately aggregates reference distributions. A theoretically ap…
Energy-Guided Continuous Entropic Barycenter Estimation for General Costs
Alexander Kolesov, Petr Mokrov, Igor Udovichenko +5
Optimal transport (OT) barycenters are a mathematically grounded way of averaging probability distributions while capturing their geometric properties. In short, the barycenter tas…
Building the Bridge of Schrödinger: A Continuous Entropic Optimal Transport Benchmark
Nikita Gushchin, Alexander Kolesov, Petr Mokrov +4
Over the last several years, there has been significant progress in developing neural solvers for the Schrödinger Bridge (SB) problem and applying them to generative modelling. Thi…
Entropic Neural Optimal Transport via Diffusion Processes
Nikita Gushchin, Alexander Kolesov, Alexander Korotin +2
We propose a novel neural algorithm for the fundamental problem of computing the entropic optimal transport (EOT) plan between continuous probability distributions which are access…
Kantorovich Strikes Back! Wasserstein GANs are not Optimal Transport?
Alexander Korotin, Alexander Kolesov, Evgeny Burnaev
Wasserstein Generative Adversarial Networks (WGANs) are the popular generative models built on the theory of Optimal Transport (OT) and the Kantorovich duality. Despite the success…