2 citations · 3 across the 14 of their papers we have counts for
13 papers · 1 filter
One-Step Generation via Riemannian Wasserstein Gradient Flows
David Li, Chanhyuk Lee, Jaehoon Yoo +7
Recently, Drifting Models and Wasserstein Gradient Flows have attracted substantial attention because they move iterative distributional refinement to training and amortize it into…
Alpha Diffusion Language Models: Factorization Alone Is Not the Problem
Nikita Gushchin, Dmitry Baranchuk, Alexander Korotin
Discrete diffusion language models can generate multiple tokens in parallel, but reducing the number of denoising steps can lead to inconsistent predictions. Standard cross-entropy…
Midpoint Generative Models
Daniil Shlenskii, Nikita Gushchin, Lev Novitskiy +2
We introduce Midpoint Generative Models (MGM), a principled framework for training one-step generative models. MGM is based on a simple symmetry of Flow Matching with linear interp…
IDLM: Inverse-distilled Diffusion Language Models
David Li, Nikita Gushchin, Dmitry Abulkhanov +4
Diffusion Language Models (DLMs) have recently achieved strong results in text generation. However, their multi-step sampling leads to slow inference, limiting practical use. To ad…
Variational Entropic Optimal Transport
Roman Dyachenko, Nikita Gushchin, Kirill Sokolov +3
Entropic optimal transport (EOT) in continuous spaces with quadratic cost is a classical tool for solving the domain translation problem. In practice, recent approaches optimize a…
Time-Correlated Video Bridge Matching
Viacheslav Vasilev, Arseny Ivanov, Nikita Gushchin +2
Bridge Matching (BM) learns a stochastic transport between two data distributions and is therefore a natural tool for data-to-data tasks such as video frame interpolation, image-to…
InfoBridge: Mutual Information estimation via Bridge Matching
Sergei Kholkin, Ivan Butakov, Evgeny Burnaev +2
Diffusion bridge models have recently become a powerful tool in the field of generative modeling. In this work, we leverage their power to address another important problem in mach…
Inverse Bridge Matching Distillation
Nikita Gushchin, David Li, Daniil Selikhanovych +3
Learning diffusion bridge models is easy; making them fast and practical is an art. Diffusion bridge models (DBMs) are a promising extension of diffusion models for applications in…
Diffusion & Adversarial Schrödinger Bridges via Iterative Proportional Markovian Fitting
Sergei Kholkin, Grigoriy Ksenofontov, David Li +6
The Iterative Markovian Fitting (IMF) procedure, which iteratively projects onto the space of Markov processes and the reciprocal class, successfully solves the Schrödinger Bridge…
Adversarial Schrödinger Bridge Matching
Nikita Gushchin, Daniil Selikhanovych, Sergei Kholkin +2
The Schrödinger Bridge (SB) problem offers a powerful framework for combining optimal transport and diffusion models. A promising recent approach to solve the SB problem is the Ite…
Light and Optimal Schrödinger Bridge Matching
Nikita Gushchin, Sergei Kholkin, Evgeny Burnaev +1
Schrödinger Bridges (SB) have recently gained the attention of the ML community as a promising extension of classic diffusion models which is also interconnected to the Entropic Op…
Light Schrödinger Bridge
Alexander Korotin, Nikita Gushchin, Evgeny Burnaev
Despite the recent advances in the field of computational Schrödinger Bridges (SB), most existing SB solvers are still heavy-weighted and require complex optimization of several ne…
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…