Zobrazeno 1 - 10
of 254
pro vyhledávání: '"Li, Jinglai"'
Equivariant Imaging (EI) regularization has become the de-facto technique for unsupervised training of deep imaging networks, without any need of ground-truth data. Observing that the EI-based unsupervised training paradigm currently has significant
Externí odkaz:
http://arxiv.org/abs/2411.05771
Identifying dynamical system (DS) is a vital task in science and engineering. Traditional methods require numerous calls to the DS solver, rendering likelihood-based or least-squares inference frameworks impractical. For efficient parameter inference
Externí odkaz:
http://arxiv.org/abs/2409.11745
Autor:
Ao, Ziqiao, Li, Jinglai
Bayesian Experimental Design (BED), which aims to find the optimal experimental conditions for Bayesian inference, is usually posed as to optimize the expected information gain (EIG). The gradient information is often needed for efficient EIG optimiz
Externí odkaz:
http://arxiv.org/abs/2308.09888
Combining the strengths of model-based iterative algorithms and data-driven deep learning solutions, deep unrolling networks (DuNets) have become a popular tool to solve inverse imaging problems. While DuNets have been successfully applied to many li
Externí odkaz:
http://arxiv.org/abs/2307.16120
Autor:
Ao, Ziqiao, Li, Jinglai
Publikováno v:
Artificial Intelligence (2023): 103954
Entropy estimation is of practical importance in information theory and statistical science. Many existing entropy estimators suffer from fast growing estimation bias with respect to dimensionality, rendering them unsuitable for high-dimensional prob
Externí odkaz:
http://arxiv.org/abs/2304.09700
Autor:
Cai, Ziruo, Tang, Junqi, Mukherjee, Subhadip, Li, Jinglai, Schönlieb, Carola Bibiane, Zhang, Xiaoqun
Bayesian methods for solving inverse problems are a powerful alternative to classical methods since the Bayesian approach offers the ability to quantify the uncertainty in the solution. In recent years, data-driven techniques for solving inverse prob
Externí odkaz:
http://arxiv.org/abs/2304.08342
The Langevin algorithms are frequently used to sample the posterior distributions in Bayesian inference. In many practical problems, however, the posterior distributions often consist of non-differentiable components, posing challenges for the standa
Externí odkaz:
http://arxiv.org/abs/2304.04544
In this work we study systems consisting of a group of moving particles. In such systems, often some important parameters are unknown and have to be estimated from observed data. Such parameter estimation problems can often be solved via a Bayesian i
Externí odkaz:
http://arxiv.org/abs/2303.14950
Solving high-dimensional Bayesian inverse problems (BIPs) with the variational inference (VI) method is promising but still challenging. The main difficulties arise from two aspects. First, VI methods approximate the posterior distribution using a si
Externí odkaz:
http://arxiv.org/abs/2302.11173
Failure probability estimation problem is an crucial task in engineering. In this work we consider this problem in the situation that the underlying computer models are extremely expensive, which often arises in the practice, and in this setting, red
Externí odkaz:
http://arxiv.org/abs/2302.06837