A precomputed lens transport model with wavelength-aware inputs, Fresnel intensity outputs, and ray occlusion masking delivers higher accuracy than polynomial baselines and runs an order of magnitude faster than full ray tracing for static symmetric lenses.
A Stochastic Approximation Method , volume =
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A unified framework for exponential tilting in diffusion and flow models that includes bias-variance decompositions showing finite gradient variance for some methods, norm bounds on adjoint ODEs, and adapted losses with new Crooks and Jarzynski identities.
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Precomputed Lens Transport Maps
A precomputed lens transport model with wavelength-aware inputs, Fresnel intensity outputs, and ray occlusion masking delivers higher accuracy than polynomial baselines and runs an order of magnitude faster than full ray tracing for static symmetric lenses.
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A unified perspective on fine-tuning and sampling with diffusion and flow models
A unified framework for exponential tilting in diffusion and flow models that includes bias-variance decompositions showing finite gradient variance for some methods, norm bounds on adjoint ODEs, and adapted losses with new Crooks and Jarzynski identities.