26 papers · 1 filter
Impute-EM: Native Mixed-State Diffusion Models for Heterogeneous Data Imputation
Sergei Kholkin, Kirill Sokolov, Dmitry Baranchuk +2
Missing values are ubiquitous in heterogeneous data mining, where numerical, categorical, and binary variables often coexist. Many imputation methods, especially diffusion-based on…
Discrete Bridges for Mutual Information Estimation
Iryna Zabarianska, Sergei Kholkin, Grigoriy Ksenofontov +2
Diffusion bridge models in both continuous and discrete state spaces have recently become powerful tools in the field of generative modeling. In this work, we leverage the discrete…
Unlocking the Duality between Flow and Field Matching
Daniil Shlenskii, Alexander Varlamov, Nazar Buzun +1
Conditional Flow Matching (CFM) unifies conventional generative paradigms such as diffusion models and flow matching. Interaction Field Matching (IFM) is a newer framework that gen…
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…
Sampling from Energy distributions with Target Concrete Score Identity
Sergei Kholkin, Francisco Vargas, Alexander Korotin
We introduce the Target Concrete Score Identity Sampler (TCSIS), a method for sampling from unnormalized densities on discrete state spaces by learning the reverse dynamics of a Co…