ActivePPI: quantifying protein-protein interaction network activity with Markov random fields. [PDF]
Bioinformatics, 2023 Wang C, Xu S, Sun D, Liu ZP.
europepmc +1 more source
Best-Choice Edge Grafting for Efficient Structure Learning of Markov Random Fields
, 2018 Incremental methods for structure learning of pairwise Markov random fields (MRFs), such as grafting, improve scalability by avoiding inference over the entire feature space in each optimization step. Instead, inference is performed over an incrementally
Chaabene, Walid, Huang, Bert
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Uncertainty Calibration in Molecular Machine Learning: Comparing Evidential and Ensemble Approaches
Chemistry – A European Journal, EarlyView.Raw uncertainty estimates from deep evidential regression and deep ensembles are systematically miscalibrated. Post hoc calibration aligns predicted uncertainty with true errors, improving reliability and enabling efficient active learning and reducing computational cost while preserving predictive accuracy.
Bidhan Chandra Garain +3 more
wiley +1 more source
Remote SensingPolSAR image classification has attracted extensive significant research in recent decades. Aiming at improving PolSAR classification performance with speckle noise, this paper proposes an active complex-valued convolutional-wavelet neural network by ...
Lu Liu, Yongxiang Li
doaj +1 more source
Improving Deep Learning based Point Cloud Classification using Markov Random Fields with Quadratic Pseudo-Boolean Optimization [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences3D point clouds are a relevant source of information for multiple applications, including digital twins, building modeling, disaster and risk management, forestry, autonomous driving, and many others.
Q. Mei, K. Qiu, D. Bulatov, D. Iwaszczuk
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Efficient Inference of Spatially-Varying Gaussian Markov Random Fields With Applications in Gene Regulatory Networks. [PDF]
IEEE/ACM Trans Comput Biol Bioinform, 2023 Ravikumar V +4 more
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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
, 2015 The theory of learning under the uniform distribution is rich and deep, with connections to cryptography, computational complexity, and the analysis of boolean functions to name a few areas.
Kanade, Varun, Mossel, Elchanan
core
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
Revisiting Gaussian Markov random fields and Bayesian disease mapping. [PDF]
Stat Methods Med Res, 2023 MacNab YC.
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