Under a Gaussian prior assumption, zero-shot diffusion posterior samplers for inverse problems admit closed-form spectral representations that enable a new parameter-selection framework balancing perceptual quality and signal fidelity.
Learning to discretize denoising diffusion odes, 2025
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.LG 2years
2026 2verdicts
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Derives discretization error bounds and input-to-state stability guarantees for SS-NOs and FNOs, with empirical validation on 1D and 2D PDE benchmarks.
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Analyzing and Guiding Zero-Shot Posterior Sampling in Diffusion Models
Under a Gaussian prior assumption, zero-shot diffusion posterior samplers for inverse problems admit closed-form spectral representations that enable a new parameter-selection framework balancing perceptual quality and signal fidelity.
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Stability and Discretization Error of State Space Model Neural Operators
Derives discretization error bounds and input-to-state stability guarantees for SS-NOs and FNOs, with empirical validation on 1D and 2D PDE benchmarks.