4 papers · 1 filter
Training-Free Generative Sampling via Moment-Matched Score Smoothing
Zhenyu Yao, Daniel Paulin
Diffusion models generate samples by denoising along the score of a perturbed target distribution. In practice, one trains a neural diffusion model, which is computationally expens…
Infinite-dimensional generative diffusions via Doob's h-transform
Thorben Pieper-Sethmacher, Daniel Paulin
This paper introduces a rigorous framework for defining generative diffusion models in infinite dimensions via Doob's h-transform. Rather than relying on time reversal of a noising…
A Semiparametric Discrete Hawkes Model with a Collapsed Gaussian-Process Prior
Trinnhallen Brisley, Gordon Ross, Daniel Paulin
Hawkes processes are used in settings where past events increase the likelihood of future events occurring, resulting in a natural clustering structure. Traditional Hawkes process…
Sampling from Bayesian Neural Network Posteriors with Symmetric Minibatch Splitting Langevin Dynamics
Daniel Paulin, Peter A. Whalley, Neil K. Chada +1
We propose a scalable kinetic Langevin dynamics algorithm for sampling parameter spaces of big data and AI applications. Our scheme combines a symmetric forward/backward sweep over…