STMD distills the full transition map of diffusion sampling SDEs into a conditional Mean Flow model to enable fast one- or few-step stochastic sampling without teacher models or bi-level optimization.
arXiv preprint arXiv:2406.14740 , year=
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Unbalanced optimal transport and unbalanced density control for Gaussian measures reduce exactly to finite-dimensional optimizations over masses, means, covariances, and SDP-solvable covariance steering with closed-form mass updates.
A lifted second-moment formulation yields an SDP for covariance steering of MJLS with multiplicative noise and convex surrogates for chance constraints.
citing papers explorer
-
Stochastic Transition-Map Distillation for Fast Probabilistic Inference
STMD distills the full transition map of diffusion sampling SDEs into a conditional Mean Flow model to enable fast one- or few-step stochastic sampling without teacher models or bi-level optimization.
-
Globally Solving Unbalanced Optimal Transport and Density Control for Gaussian Distributions
Unbalanced optimal transport and unbalanced density control for Gaussian measures reduce exactly to finite-dimensional optimizations over masses, means, covariances, and SDP-solvable covariance steering with closed-form mass updates.
-
Covariance Steering of Discrete-Time Markov Jump Linear Systems with Multiplicative Noise
A lifted second-moment formulation yields an SDP for covariance steering of MJLS with multiplicative noise and convex surrogates for chance constraints.