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This paper presents a theoretical framework interpreting Transformer components (attention, residual connections, normalization) as arising from a spherical state estimation problem using Radial-Tangential SDEs.
This paper introduces Christoffel-DPS, a distribution-free framework for optimal sensor placement in diffusion posterior sampling that outperforms classical Gaussian-based methods. It provides theoretical guarantees and practical improvements for reconstructing states from complex, non-Gaussian distributions using generative models.