3 articles tagged with #posterior-sampling. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers have identified flaws in existing test-time guidance methods for diffusion models that prevent proper Bayesian posterior sampling. They propose new estimators that enable calibrated inference, significantly outperforming previous methods on Bayesian tasks and matching state-of-the-art results in black hole image reconstruction.
Researchers introduce Flow Annealing Posterior Sampling (FAPS), a new function-space framework that unifies stochastic-process regression with PDE inverse problems using pretrained flow-matching priors. The method enables probabilistic inference from sparse observations while maintaining computational efficiency and accurate uncertainty quantification, outperforming existing baselines.
Researchers propose PS-PFN, an advanced AutoML method that extends traditional algorithm selection and hyperparameter optimization to handle modern ML pipelines with fine-tuning and ensembling. Using posterior sampling and prior-data fitted networks for in-context learning, the approach outperforms existing bandit and AutoML strategies on benchmark tasks.
