35 citations · 73 across the 10 of their papers we have counts for
5 papers · 1 filter
Metropolis Sampling for Constrained Diffusion Models
Nic Fishman, Leo Klarner, Emile Mathieu +2
Denoising diffusion models have recently emerged as the predominant paradigm for generative modelling on image domains. In addition, their extension to Riemannian manifolds has fac…
Diffusion Models for Constrained Domains
Nic Fishman, Leo Klarner, Valentin De Bortoli +2
Denoising diffusion models are a novel class of generative algorithms that achieve state-of-the-art performance across a range of domains, including image generation and text-to-im…
Riemannian Score-Based Generative Modelling
Valentin De Bortoli, Emile Mathieu, Michael Hutchinson +3
Score-based generative models (SGMs) are a powerful class of generative models that exhibit remarkable empirical performance. Score-based generative modelling (SGM) consists of a `…
LieTransformer: Equivariant self-attention for Lie Groups
Michael Hutchinson, Charline Le Lan, Sheheryar Zaidi +3
Group equivariant neural networks are used as building blocks of group invariant neural networks, which have been shown to improve generalisation performance and data efficiency th…
Equivariant Learning of Stochastic Fields: Gaussian Processes and Steerable Conditional Neural Processes
Peter Holderrieth, Michael Hutchinson, Yee Whye Teh
Motivated by objects such as electric fields or fluid streams, we study the problem of learning stochastic fields, i.e. stochastic processes whose samples are fields like those occ…