Presents a general framework for generator matching on projected image spaces from latent Markov processes, generalizing static latent results to dynamic conditional processes.
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TRACE creates valid conformal prediction sets for complex generative models by scoring outputs via averaged denoising or velocity errors along stochastic transport paths instead of likelihoods.
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Latent Process Generator Matching
Presents a general framework for generator matching on projected image spaces from latent Markov processes, generalizing static latent results to dynamic conditional processes.
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TRACE: Transport Alignment Conformal Prediction via Diffusion and Flow Matching Models
TRACE creates valid conformal prediction sets for complex generative models by scoring outputs via averaged denoising or velocity errors along stochastic transport paths instead of likelihoods.
- Spherical Flows for Sampling Categorical Data