Light and Optimal Schrödinger Bridge Matching

1 code implementation • 5 Feb 2024 • Nikita Gushchin, Sergei Kholkin, Evgeny Burnaev, Alexander Korotin

It exploits the optimal parameterization of the diffusion process and provably recovers the SB process \textbf{(a)} with a single bridge matching step and \textbf{(b)} with arbitrary transport plan as the input.

Light Schrödinger Bridge

1 code implementation • 2 Oct 2023 • Alexander Korotin, Nikita Gushchin, Evgeny Burnaev

Despite the recent advances in the field of computational Schr\"odinger Bridges (SB), most existing SB solvers are still heavy-weighted and require complex optimization of several neural networks.

Density Estimation

Building the Bridge of Schrödinger: A Continuous Entropic Optimal Transport Benchmark

1 code implementation • NeurIPS 2023 • Nikita Gushchin, Alexander Kolesov, Petr Mokrov, Polina Karpikova, Andrey Spiridonov, Evgeny Burnaev, Alexander Korotin

We fill this gap and propose a novel way to create pairs of probability distributions for which the ground truth OT solution is known by the construction.

Energy-guided Entropic Neural Optimal Transport

1 code implementation • 12 Apr 2023 • Petr Mokrov, Alexander Korotin, Alexander Kolesov, Nikita Gushchin, Evgeny Burnaev

Energy-based models (EBMs) are known in the Machine Learning community for decades.

Generalization Bounds Image-to-Image Translation

Entropic Neural Optimal Transport via Diffusion Processes

1 code implementation • NeurIPS 2023 • Nikita Gushchin, Alexander Kolesov, Alexander Korotin, Dmitry Vetrov, Evgeny Burnaev

We propose a novel neural algorithm for the fundamental problem of computing the entropic optimal transport (EOT) plan between continuous probability distributions which are accessible by samples.