Brian L Trippe | DeepAI

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∙ 06/30/2023

Practical and Asymptotically Exact Conditional Sampling in Diffusion Models

Diffusion models have been successful on a range of conditional generati...

0 Luhuan Wu, et al. ∙

∙ 02/05/2023

SE(3) diffusion model with application to protein backbone generation

The design of novel protein structures remains a challenge in protein en...

0 Jason Yim, et al. ∙

∙ 06/08/2022

Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem

Construction of a scaffold structure that supports a desired motif, conf...

15 Brian L Trippe, et al. ∙

∙ 02/23/2022

Many processors, little time: MCMC for partitions via optimal transport couplings

Markov chain Monte Carlo (MCMC) methods are often used in clustering sin...

0 Tin D. Nguyen, et al. ∙

∙ 07/13/2021

For high-dimensional hierarchical models, consider exchangeability of effects across covariates instead of across datasets

Hierarchical Bayesian methods enable information sharing across multiple...

0 Brian L Trippe, et al. ∙

∙ 04/09/2021

Optimal transport couplings of Gibbs samplers on partitions for unbiased estimation

Computational couplings of Markov chains provide a practical route to un...

0 Brian L Trippe, et al. ∙

∙ 02/19/2021

Confidently Comparing Estimators with the c-value

Modern statistics provides an ever-expanding toolkit for estimating unkn...

0 Brian L Trippe, et al. ∙

∙ 05/17/2019

LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations

Due to the ease of modern data collection, applied statisticians often h...

0 Brian L Trippe, et al. ∙

∙ 02/14/2018

Conditional Density Estimation with Bayesian Normalising Flows

Modeling complex conditional distributions is critical in a variety of s...

0 Brian L Trippe, et al. ∙

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