Effective Method for Inverse Ising Problem under Missing Observations in Restricted Boltzmann Machines
Restricted Boltzmann machines (RBMs) are energy-based models analogous to the Ising model and are widely applied in statistical machine learning. The standard inverse Ising problem with a complete dataset requires computing both data and model expectations and is computationally challenging because model expectations have a combinatorial explosion. Furthermore, in many applications, the available datasets are partially incomplete, making it difficult to compute even data expectations. In this study, we propose a approximation framework for these expectations in the practical inverse Ising problems that integrates mean-field approximation or persistent contrastive divergence to generate refined initial points and spatial Monte Carlo integration to enhance estimator accuracy. We demonstrate that the proposed method effectively and accurately tunes the model parameters in comparison to the conventional method.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Matrix Completion under Low-Rank Missing Mechanism
Matrix completion is a modern missing data problem where both the missing structure and the underlying parameter are high dimensional. Although missing structure is a key component to any missing data problems, existing …
Matrix CompletionStochastic Solutions for Simultaneous Seismic Data Denoising and Reconstruction via Score-based Generative Models
Usually, inverse problems are ill-posed. The solution to the inverse problem is indeterminate, meaning that for given observational data, there may be multiple possible solutions. It is not sufficient to give a definite …
DenoisingGeophysicsImage GenerationTenIPS: Inverse Propensity Sampling for Tensor Completion
Tensors are widely used to represent multiway arrays of data. The recovery of missing entries in a tensor has been extensively studied, generally under the assumption that entries are missing completely at random (MCAR).…
Missing ValuesA Bayesian Hyperprior Approach for Joint Image Denoising and Interpolation, with an Application to HDR Imaging
Recently, impressive denoising results have been achieved by Bayesian approaches which assume Gaussian models for the image patches. This improvement in performance can be attributed to the use of per-patch models. Unfor…
DenoisingImage DenoisingBayesian ECG reconstruction using denoising diffusion generative models
In this work, we propose a denoising diffusion generative model (DDGM) trained with healthy electrocardiogram (ECG) data that focuses on ECG morphology and inter-lead dependence. Our results show that this innovative gen…
Denoising