Michael Hutchinson | DeepAI

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∙ 07/11/2023

Metropolis Sampling for Constrained Diffusion Models

Denoising diffusion models have recently emerged as the predominant para...

0 Nic Fishman, et al. ∙

∙ 04/11/2023

Diffusion Models for Constrained Domains

Denoising diffusion models are a recent class of generative models which...

0 Nic Fishman, et al. ∙

∙ 09/28/2022

Spectral Diffusion Processes

Score-based generative modelling (SGM) has proven to be a very effective...

8 Angus Phillips, et al. ∙

∙ 07/07/2022

Riemannian Diffusion Schrödinger Bridge

Score-based generative models exhibit state of the art performance on de...

32 James Thornton, et al. ∙

∙ 02/06/2022

Riemannian Score-Based Generative Modeling

Score-based generative models (SGMs) are a novel class of generative mod...

5 Valentin De Bortoli, et al. ∙

∙ 10/27/2021

Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernels

Gaussian processes are machine learning models capable of learning unkno...

15 Michael Hutchinson, et al. ∙

∙ 01/07/2021

Enhanced Direct Delta Mush

Direct Delta Mush is a novel skinning deformation technique introduced b...

0 Serguei Kalentchouk, et al. ∙

∙ 12/20/2020

LieTransformer: Equivariant self-attention for Lie Groups

Group equivariant neural networks are used as building blocks of group i...

6 Michael Hutchinson, et al. ∙

∙ 11/25/2020

Equivariant Conditional Neural Processes

We introduce Equivariant Conditional Neural Processes (EquivCNPs), a new...

10 Peter Holderrieth, et al. ∙

∙ 11/24/2019

Differentially Private Federated Variational Inference

In many real-world applications of machine learning, data are distribute...

16 Mrinank Sharma, et al. ∙

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