71 citations · 276 across the 43 of their papers we have counts for
6 papers · 2 filters
From Denoising Diffusions to Denoising Markov Models
Joe Benton, Yuyang Shi, Valentin De Bortoli +2
Denoising diffusions are state-of-the-art generative models exhibiting remarkable empirical performance. They work by diffusing the data distribution into a Gaussian distribution a…
Unbiased constrained sampling with Self-Concordant Barrier Hamiltonian Monte Carlo
Maxence Noble, Valentin De Bortoli, Alain Durmus
In this paper, we propose Barrier Hamiltonian Monte Carlo (BHMC), a version of the HMC algorithm which aims at sampling from a Gibbs distribution on a manifold , en…
Spectral Diffusion Processes
Angus Phillips, Thomas Seror, Michael Hutchinson +3
Diffusion models have proven to be a flexible and effective framework for modelling probability distributions on finite-dimensional spaces. However, many physical modelling problem…
Riemannian Diffusion Schrödinger Bridge
James Thornton, Michael Hutchinson, Emile Mathieu +3
Score-based generative models exhibit state of the art performance on density estimation and generative modeling tasks. These models typically assume that the data geometry is flat…
A Continuous Time Framework for Discrete Denoising Models
Andrew Campbell, Joe Benton, Valentin De Bortoli +3
We provide the first complete continuous time framework for denoising diffusion models of discrete data. This is achieved by formulating the forward noising process and correspondi…
Conditional Simulation Using Diffusion Schrödinger Bridges
Yuyang Shi, Valentin De Bortoli, George Deligiannidis +1
Denoising diffusion models have recently emerged as a powerful class of generative models. They provide state-of-the-art results, not only for unconditional simulation, but also wh…