Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 17 of 17 for “"Loopy"”.
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Belief propagation on factor graph neural networks
… can be used to compute an exact solution for non-loopy factor graphs. However, when applied to loopy factor graphs, it only estimates marginal probabilities approximately. In this thesis, we propose a Graph Neural Networks (GNN) approach for belief propagation based on message passing mechanisms. …
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Extending expectation propagation for graphical models
… propagation, a powerful generalization of loopy belief propagation, to develop efficient Bayesian inference and learning algorithms for graphical models. The first two chapters of the thesis present inference algorithms for generative graphical models, and the next two propose learning …
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Feedback message passing for inference in Gaussian graphical models
For Gaussian graphical models with cycles, loopy belief propagation often performs reasonably well, but its convergence is not guaranteed and the computation of variances is generally incorrect. In this paper, we identify a set of special vertices called a feedback vertex set whose removal results …
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Inference in Ising models by graph neural networks with structural features
… (GNNs) are shown to outperform BP on small-scale loopy graphs. GNN computes a more general function on each node using neural networks, and learns the exact distribution of small loop-free and loopy graphs. As BP is exact on loop-free graphs and graphs with exactly one loop, GNN performs worse …
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Sparse Matrix Belief Propagation
… sparse-matrix belief propagation, which executes loopy belief propagation in Markov random fields by replacing indexing over graph neighborhoods with sparse-matrix operations. This abstraction allows for seamless integration with optimized sparse linear algebra libraries, including those that …
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Robust and efficient image-based 3D modeling
… a new framework based on bilateral filtering and loopy belief propagation for simultaneous estimation of surface reflectance and shape with the assumption that the illumination chromaticity can be correctly estimated. Two new bilateral filtering algorithms with computational complexity invariant …
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Energy-efficient information inference in wireless sensor networks based on graphical modeling
… (IPF). When the MRF model is constructed, Loopy Belief Propagation (LBP) is then employed to perform information inference to estimate the missing data given incomplete network observations. The proposed approach is then improved in terms of energy-efficiency and robustness from three …
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Belief propagation generative adversarial networks
… and perform probabilistic inference using loopy belief propagation on continuous Markov random fields. Experiments on the MNIST dataset show that our model is able to outperform vanilla GANs with more than two iterations of belief propagation.
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Approximate inference in Gaussian graphical models
… approximate inference algorithm called loopy belief propagation (LBP), and establish conditions for its convergence. We also extend the walk-sum framework to analyze more powerful versions of LBP that trade off convergence and accuracy for computational complexity, and establish …
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Learning with Degree-Based Subgraph Estimation
… of convergence for belief propagation on the loopy graphical model representing the b-matching objective. Additionally, this thesis describes new algorithmic techniques to improve the scalability of the b-matching solver. In addition to various applications of node degree in machine learning, …
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Low-rank graphical models and Bayesian inference in the statistical analysis of noisy neural data
… the circle. Because the orientation map model is loopy, we cannot do exact inference on the low-rank model by the forward backward algorithm, but block-wise Gibbs sampling by the forward backward algorithm speeds mixing. We explore another von Mises coupling potential Gibbs sampler that proves to …
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Probabilistic graphical models : distributed inference and learning models with small feedback vertex sets
… are given. For inference in graphs with cycles, loopy belief propagation (LBP) is a purely distributed algorithm, but it gives inaccurate variance estimates in general and often diverges or has slow convergence. Previously, the hybrid feedback message passing (FMP) algorithm was developed to …
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Theoretical guarantees and complexity reduction in information planning
… interest. We also provide extensions to Gaussian loopy graphs and to the problem of fining the most likely sequence of hidden variables.
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Monte Carlo integration in discrete undirected probabilistic models
… and the naive Gibbs sampler, even in cases where loopy belief propagation fails to converge. We prove that tree sampling exhibits lower variance than the naive Gibbs sampler and other naive partitioning schemes using the theoretical measure of maximal correlation. We also construct new information …
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Deep Learning for Brain Structural Connectivity Analysis: From Tissue Segmentation to Tractogram Alignment
… graph convolutional networks and differentiable loopy belief propagation, incorporating the definition of fiber structure into the encoding of the graph. Our empirical analysis demonstrates the advantages of utilizing the proposed GDL framework over traditional volumetric registration, showcasing …
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Hierarchical Bayesian approaches to seismic imaging and other geophysical inverse problems
… of a fault). We solve the inference problem via loopy belief propagation to approximate the posterior marginal distributions of the fracture properties, as well as their maximum a posteriori (MAP) and Bayes least squares estimates. In the second part of the thesis, we investigate how the …
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Exascale finite/spectral element simulations
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-08-01