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Found 5 papers, 4 papers with code
Meta Flow Matching: Integrating Vector Fields on the Wasserstein Manifold
Flow-based models allow for learning these dynamics at the population level - they model the evolution of the entire distribution of samples.
Investigating Generalization Behaviours of Generative Flow Networks
Since their inception, GFlowNets have proven to be useful for learning generative models in applications where the majority of the discrete space is unvisited during training.
A Computational Framework for Solving Wasserstein Lagrangian Flows
The dynamical formulation of the optimal transport can be extended through various choices of the underlying geometry (kinetic energy), and the regularization of density paths (potential energy).
Simulation-free Schrödinger bridges via score and flow matching
We present simulation-free score and flow matching ([SF]$^2$M), a simulation-free objective for inferring stochastic dynamics given unpaired samples drawn from arbitrary source and target distributions.
DynGFN: Towards Bayesian Inference of Gene Regulatory Networks with GFlowNets
In this paper we leverage the fact that it is possible to estimate the "velocity" of gene expression with RNA velocity techniques to develop an approach that addresses both challenges.
