Combining Convolutional Neural Network and Markov Random Field for Semantic Image Retrieval
Advances in Multimedia, 2018 With the rapidly growing number of images over the Internet, efficient scalable semantic image retrieval becomes increasingly important. This paper presents a novel approach for semantic image retrieval by combining Convolutional Neural Network (CNN) and
Haijiao Xu +4 more
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Hidden Markov random field models for cell-type assignment of spatially resolved transcriptomics. [PDF]
Bioinformatics, 2023 Zhong C, Tian T, Wei Z.
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Bayesian segmentation of hyperspectral images
, 2007 In this paper we consider the problem of joint segmentation of hyperspectral images in the Bayesian framework. The proposed approach is based on a Hidden Markov Modeling (HMM) of the images with common segmentation, or equivalently with common hidden ...
Féron, Olivier +2 more
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Which graphical models are difficult to learn? [PDF]
, 2009 We consider the problem of learning the structure of Ising models (pairwise binary Markov random fields) from i.i.d. samples. While several methods have been proposed to accomplish this task, their relative merits and limitations remain somewhat obscure.
Bento, Jose, Montanari, Andrea
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Restricted Tweedie stochastic block models
Canadian Journal of Statistics, EarlyView.Abstract The stochastic block model (SBM) is a widely used framework for community detection in networks, where the network structure is typically represented by an adjacency matrix. However, conventional SBMs are not directly applicable to an adjacency matrix that consists of nonnegative zero‐inflated continuous edge weights.
Jie Jian, Mu Zhu, Peijun Sang
wiley +1 more source
Some Shannon-McMillan Approximation Theorems for Markov Chain Field on the Generalized Bethe Tree
Journal of Inequalities and Applications, 2011 A class of small-deviation theorems for the relative entropy densities of arbitrary random field on the generalized Bethe tree are discussed by comparing the arbitrary measure with the Markov measure on the generalized Bethe tree.
Zong Decai, Wang Kangkang
doaj
Adaptive Gaussian Markov random field spatiotemporal models for infectious disease mapping and forecasting. [PDF]
Spat Stat, 2023 MacNab YC.
europepmc +1 more source
Hidden Markov graphical models with state‐dependent generalized hyperbolic distributions
Canadian Journal of Statistics, EarlyView.Abstract In this article, we develop a novel hidden Markov graphical model to investigate time‐varying interconnectedness between different financial markets. To identify conditional correlation structures under varying market conditions and accommodate shape features embedded in financial time series, we rely upon the generalized hyperbolic family of ...
Beatrice Foroni +2 more
wiley +1 more source
Probabilistic edge inference of gene networks with markov random field-based bayesian learning. [PDF]
Front Genet, 2022 Huang YJ, Mukherjee R, Hsiao CK.
europepmc +1 more source
Relaxations for inference in restricted Boltzmann machines [PDF]
, 2014 We propose a relaxation-based approximate inference algorithm that samples near-MAP configurations of a binary pairwise Markov random field. We experiment on MAP inference tasks in several restricted Boltzmann machines. We also use our underlying sampler
Frostig, Roy +3 more
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