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New StAD method speeds up generative model likelihood calculations

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new method called StAD to improve the speed and accuracy of likelihood calculations in diffusion and flow-based generative models. This technique bypasses the need to compute the Jacobian of the probability flow ODE, instead learning the divergence directly using the Langevin-Stein operator. StAD has demonstrated competitive performance against existing methods like Hutchinson and Hutch++ on various density estimation tasks, showing improved variance and speed. AI

IMPACT Accelerates likelihood computation for diffusion and flow-based models, benefiting Bayesian analysis and density estimation tasks.

RANK_REASON The cluster contains an academic paper detailing a new method for generative models.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New StAD method speeds up generative model likelihood calculations

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COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Gurjeet Jagwani, Stephen Thorp, Sinan Deger, Hiranya Peiris ·

    StAD: Stein Amortized Divergence for Fast Likelihoods with Diffusion and Flow

    arXiv:2605.16486v1 Announce Type: new Abstract: 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 (C…

  2. arXiv stat.ML TIER_1 English(EN) · Hiranya Peiris ·

    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 proba…

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