Results 1 to 10 of about 325,747 (265)
On Graphical Models and Convex Geometry. [PDF]
Comput Stat Data Anal, 2023 We introduce a mixture-model of beta distributions to identify significant correlations among $P$ predictors when $P$ is large. The method relies on theorems in convex geometry, which we use to show how to control the error rate of edge detection in graphical models. Our `betaMix' method does not require any assumptions about the network structure, nor
Bar H, Wells MT.
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Statistical Science, 2004 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Michael Jordan
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Graphical Models for Extremes [PDF]
Journal of the Royal Statistical Society Series B: Statistical Methodology, 2020 SummaryConditional independence, graphical models and sparsity are key notions for parsimonious statistical models and for understanding the structural relationships in the data. The theory of multivariate and spatial extremes describes the risk of rare events through asymptotically justified limit models such as max-stable and multivariate Pareto ...
Sebastian Engelke
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PU-GAT: Point cloud upsampling with graph attention network
Graphical Models, 2023 Point cloud upsampling has been extensively studied, however, the existing approaches suffer from the losing of structural information due to neglect of spatial dependencies between points.
Xuan Deng +3 more
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Unsupervised learning of style-aware facial animation from real acting performances
Graphical Models, 2023 This paper presents a novel approach for text/speech-driven animation of a photo-realistic head model based on blend-shape geometry, dynamic textures, and neural rendering.
Wolfgang Paier +2 more
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Fast progressive polygonal approximations for online strokes
Graphical Models, 2023 This paper presents a fast and progressive polygonal approximation algorithm for online strokes. A stroke is defined as a sequence of points between a pen-down and a pen-up. The proposed method generates polygonal approximations progressively as the user
Mohammad Tanvir Parvez
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Joint data and feature augmentation for self-supervised representation learning on point clouds
Graphical Models, 2023 To deal with the exhausting annotations, self-supervised representation learning from unlabeled point clouds has drawn much attention, especially centered on augmentation-based contrastive methods.
Zhuheng Lu +3 more
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RFMNet: Robust Deep Functional Maps for unsupervised non-rigid shape correspondence
Graphical Models, 2023 In traditional deep functional maps for non-rigid shape correspondence, estimating a functional map including high-frequency information requires enough linearly independent features via the least square method, which is prone to be violated in practice,
Ling Hu +5 more
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Unified shape and appearance reconstruction with joint camera parameter refinement
Graphical Models, 2023 In this paper, we present an inverse rendering method for the simple reconstruction of shape and appearance of real-world objects from only roughly calibrated RGB images captured under collocated point light illumination.
Julian Kaltheuner +2 more
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High-fidelity point cloud completion with low-resolution recovery and noise-aware upsampling
Graphical Models, 2023 Completing an unordered partial point cloud is a challenging task. Existing approaches that rely on decoding a latent feature to recover the complete shape, often lead to the completed point cloud being over-smoothing, losing details, and noisy.
Ren-Wu Li +4 more
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