9 citations · 11 across the 7 of their papers we have counts for
5 papers · 1 filter
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…
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…
Overclocking Electrostatic Generative Models
Daniil Shlenskii, Alexander Korotin
Electrostatic generative models such as PFGM++ have recently emerged as a powerful framework, achieving competitive performance in image synthesis. PFGM++ operates in an extended d…
HOTA: Hamiltonian framework for Optimal Transport Advection
Nazar Buzun, Daniil Shlenskii, Maxim Bobrin +1
Optimal transport (OT) has become a natural framework for guiding the probability flows. Yet, the majority of recent generative models assume trivial geometry (e.g., Euclidean) and…
TabR: Tabular Deep Learning Meets Nearest Neighbors in 2023
Yury Gorishniy, Ivan Rubachev, Nikolay Kartashev +3
Deep learning (DL) models for tabular data problems (e.g. classification, regression) are currently receiving increasingly more attention from researchers. However, despite the rec…