2 citations · 2 across the 4 of their papers we have counts for
4 papers
Noise-Adaptive Diffusion Sampling for Inverse Problems Without Task-Specific Tuning
Yingzhi Xia, Setthakorn Tanomkiattikun, Liangli Zhen +1
Diffusion models (DMs) have recently shown remarkable performance on inverse problems (IPs). Optimization-based methods can fast solve IPs using DMs as powerful regularizers, but t…
VI-DGP: A variational inference method with deep generative prior for solving high-dimensional inverse problems
Yingzhi Xia, Qifeng Liao, Jinglai Li
Solving high-dimensional Bayesian inverse problems (BIPs) with the variational inference (VI) method is promising but still challenging. The main difficulties arise from two aspect…
A domain-decomposed VAE method for Bayesian inverse problems
Zhihang Xu, Yingzhi Xia, Qifeng Liao
Bayesian inverse problems are often computationally challenging when the forward model is governed by complex partial differential equations (PDEs). This is typically caused by exp…
A deep domain decomposition method based on Fourier features
Sen Li, Yingzhi Xia, Yu Liu +1
In this paper we present a Fourier feature based deep domain decomposition method (F-D3M) for partial differential equations (PDEs). Currently, deep neural network based methods ar…