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We use the smooth saturation: v7→v·τtanh(∥v∥/τ)/(∥v∥+1e −8), where τ= 1×10 2 is a maximum norm threshold

We clip the norm of the vector field (after scaling by− 1 2 β(t) as prescribed by the probability flow ODE (16)) to limit excessively large steps to ensure stable integration

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Conditioning Gaussian Processes on Almost Anything

stat.ML · 2026-05-20 · unverdicted · novelty 7.0

Equivalence between Gaussian processes and linear diffusion models enables general conditioning on arbitrary pointwise likelihoods via ODE dynamics and Monte Carlo guidance approximation.

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  • Conditioning Gaussian Processes on Almost Anything stat.ML · 2026-05-20 · unverdicted · none · ref 69

    Equivalence between Gaussian processes and linear diffusion models enables general conditioning on arbitrary pointwise likelihoods via ODE dynamics and Monte Carlo guidance approximation.