9 papers
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…
On the Equivalence of Optimal Transport Problem and Action Matching with Optimal Vector Fields
Nikita Kornilov, Alexander Korotin
Flow Matching (FM) method in generative modeling maps arbitrary probability distributions by constructing an interpolation between them and then learning the vector field that defi…
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…
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…
Exponential convergence rate for Iterative Markovian Fitting
Kirill Sokolov, Alexander Korotin
We consider the discrete-time Schrödinger bridge problem on a finite state space. Although it has been known that the Iterative Markovian Fitting (IMF) algorithm converges in Kullb…
Risk-Averse Reinforcement Learning with Itakura-Saito Loss
Igor Udovichenko, Olivier Croissant, Anita Toleutaeva +2
Risk-averse reinforcement learning finds application in various high-stakes fields. Unlike classical reinforcement learning, which aims to maximize expected returns, risk-averse ag…