StAD: Stein Amortized Divergence for Fast Likelihoods with Diffusion and Flow
Diffusion and flow-based models are ubiquitously used for generative modelling and density estimation. They admit a deterministic probability flow ordinary differential equation (PF-ODE), analogous to continuous normalizing flows (CNFs), which describes the transport of the probability mass. Obtaining the likelihood from these models is of interest to many workflows, especially Bayesian analysis, and requires solving the trace of the Jacobian to compute the divergence of the learned PF-ODE, which is either $\mathcal{O}(D^2)$ to compute exactly or $\mathcal{O}(D)$ with a noisy estimate. We introduce StAD, a new distillation method to predict and learn the divergence of the PF-ODE using the...
| Authors | Gurjeet Jagwani, Stephen Thorp, Sinan Deger et al. |
|---|---|
| Primary category | stat.ML |
| Published | 15 May 2026 |
| Last updated at source | 15 May 2026 |
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