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Found 24 papers, 2 papers with code
Improving Interpretability of Scores in Anomaly Detection Based on Gaussian-Bernoulli Restricted Boltzmann Machine
In GBRBM-based anomaly detection, normal and anomalous data are classified based on a score that is identical to an energy function of the marginal GBRBM.
Semi-supervised Anomaly Detection
Supervised Anomaly Detection
Multi-layered Discriminative Restricted Boltzmann Machine with Untrained Probabilistic Layer
Inspired by the idea of ELM, a probabilistic untrained layer called a probabilistic-ELM (PELM) layer is proposed, and it is combined with a discriminative restricted Boltzmann machine (DRBM), which is a probabilistic three-layered neural network for solving classification problems.
Free Energy Evaluation Using Marginalized Annealed Importance Sampling
The evaluation of the free energy of a stochastic model is considered a significant issue in various fields of physics and machine learning.
Composite Spatial Monte Carlo Integration Based on Generalized Least Squares
In SMCI, the multiple summation for the variables in the sum region is precisely executed, and that in the outer region is evaluated by the sampling approximation such as the standard Monte Carlo integration.
Spatial Monte Carlo Integration with Annealed Importance Sampling
However, the estimations obtained using SMCI (and MCI) exhibit a low accuracy in Ising models under a low temperature owing to degradation of the sampling quality.
A Generalization of Spatial Monte Carlo Integration
However, SMCI as proposed in the previous studies suffers from a limitation that prevents the application of a higher-order method to dense systems.
Consistent Batch Normalization for Weighted Loss in Imbalanced-Data Environment
A combination of WLF and batch normalization (BN) is considered in this study.
Improvement of Batch Normalization in Imbalanced Data
We consider a combination of weighted loss function and batch normalization (BN) in this study.
Empirical Bayes Method for Boltzmann Machines
In this study, we consider an empirical Bayes method for Boltzmann machines and propose an algorithm for it.
Restricted Boltzmann Machine with Multivalued Hidden Variables: a model suppressing over-fitting
Generalization is one of the most important issues in machine learning problems.
Momentum-Space Renormalization Group Transformation in Bayesian Image Modeling by Gaussian Graphical Model
A new Bayesian modeling method is proposed by combining the maximization of the marginal likelihood with a momentum-space renormalization group transformation for Gaussian graphical models.
Susceptibility Propagation by Using Diagonal Consistency
A susceptibility propagation that is constructed by combining a belief propagation and a linear response method is used for approximate computation for Markov random fields.
Linear-Time Algorithm in Bayesian Image Denoising based on Gaussian Markov Random Field
In this paper, we consider Bayesian image denoising based on a Gaussian Markov random field (GMRF) model, for which we propose an new algorithm.
Solving Non-parametric Inverse Problem in Continuous Markov Random Field using Loopy Belief Propagation
The exact treatment of maximum likelihood estimation is intractable because of two problems: (1) it includes the evaluation of the partition function and (2) it is formulated in the form of functional optimization.
Community Detection Algorithm Combining Stochastic Block Model and Attribute Data Clustering
We propose a new algorithm to detect the community structure in a network that utilizes both the network structure and vertex attribute data.
Effective Mean-Field Inference Method for Nonnegative Boltzmann Machines
Nonnegative Boltzmann machines (NNBMs) are recurrent probabilistic neural network models that can describe multi-modal nonnegative data.
Mean-Field Inference in Gaussian Restricted Boltzmann Machine
A Gaussian restricted Boltzmann machine (GRBM) is a Boltzmann machine defined on a bipartite graph and is an extension of usual restricted Boltzmann machines.
Statistical Analysis of Loopy Belief Propagation in Random Fields
In the latter part of this paper, we describe the application of the proposed method to Bayesian image restoration, in which we observed that our theoretical results are in good agreement with the numerical results for natural images.
Inverse Renormalization Group Transformation in Bayesian Image Segmentations
A new Bayesian image segmentation algorithm is proposed by combining a loopy belief propagation with an inverse real space renormalization group transformation to reduce the computational time.
Boltzmann-Machine Learning of Prior Distributions of Binarized Natural Images
Prior distributions of binarized natural images are learned by using a Boltzmann machine.
Composite Likelihood Estimation for Restricted Boltzmann machines
Learning the parameters of graphical models using the maximum likelihood estimation is generally hard which requires an approximation.
Bayesian Reconstruction of Missing Observations
We focus on an interpolation method referred to Bayesian reconstruction in this paper.
Bayesian image segmentations by Potts prior and loopy belief propagation
This paper presents a Bayesian image segmentation model based on Potts prior and loopy belief propagation.
Traffic data reconstruction based on Markov random field modeling
We consider the traffic data reconstruction problem.
