Results 91 to 100 of about 22,384 (303)
Learning Algorithm of Boltzmann Machine Based on Spatial Monte Carlo Integration Method
Algorithms, 2018 The machine learning techniques for Markov random fields are fundamental in various fields involving pattern recognition, image processing, sparse modeling, and earth science, and a Boltzmann machine is one of the most important models in Markov random ...
Muneki Yasuda
doaj +1 more source
Rank-driven Markov processes [PDF]
, 2011 We study a class of Markovian systems of N elements taking values in [0,1] that evolve in discrete time t via randomized replacement rules based on the ranks of the elements.
Grinfeld, Michael +7 more
core +2 more sources
Advanced Experiment Design Strategies for Drug Development
Advanced Intelligent Discovery, EarlyView.Wang et al. analyze 592 drug development studies published between 2020 and 2024 that applied design of experiments methodologies. The review surveys both classical and emerging approaches—including Bayesian optimization and active learning—and identifies a critical gap between advanced experimental strategies and their practical adoption in ...
Fanjin Wang +3 more
wiley +1 more source
, 2022 S.297-310Prior models of image or scene structure are useful for dealing with "noise" and ambiguity that occur in many machine vision problems such as stereo, optical flow, denoising, superresolution, and surface reconstruction.
Roth, Stefan, Black, Michael
core
Advanced Intelligent Discovery, EarlyView.Heat generation in lithium‐ion batteries affects performance, aging, and safety, requiring accurate thermal modeling. Traditional methods face efficiency and adaptability challenges. This article reviews machine learning‐based and hybrid modeling approaches, integrating data and physics to improve parameter estimation and temperature prediction ...
Qi Lin +4 more
wiley +1 more source
Composition‐Aware Cross‐Sectional Integration for Spatial Transcriptomics
Advanced Intelligent Discovery, EarlyView.Multi‐section spatial transcriptomics demands coherent cell‐type deconvolution, domain detection, and batch correction, yet existing pipelines treat these tasks separately. FUSION unifies them within a composition‐aware latent framework, modeling reads as cell‐type–specific topics and clustering in embedding space.
Qishi Dong +5 more
wiley +1 more source
Stochastic monotonicity and duality for one-dimensional Markov processes [PDF]
, 2011 The theory of monotonicity and duality is developed for general one-dimensional Feller processes, extending the approach from [11]. Moreover it is shown that local monotonicity conditions (conditions on the Lévy kernel) are sufficient to prove the well-
Kolokoltsov, V. N. (Vasiliĭ Nikitich)
core +1 more source
Overcoming the Nyquist Limit in Molecular Hyperspectral Imaging by Reinforcement Learning
Advanced Intelligent Discovery, EarlyView.Explorative spectral acquisition guide automatically selects informative spectral bands to optimize downstream tasks, outperforming full‐spectrum acquisition. The selected hyperspectral data are used for tasks such as unmixing and segmentation. BandOptiNet encodes selection states and outputs optimal bands to guide spectral acquisition. Recent advances
Xiaobin Tang +4 more
wiley +1 more source
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingThis article presents a novel hyperspectral image (HSI) classification approach that integrates the sparse inducing variational Gaussian process (SIVGP) with a spatially adaptive Markov random field (SAMRF), termed G-MDRF.
Yaqiu Zhang, Lizhi Liu, Xinnian Yang
doaj +1 more source
Graphical Models Over Heterogeneous Domains and for Multilevel Networks
IEEE Access, 2018 We review models for analyzing multivariate data of mixed (heterogeneous) domains such as binary, categorical, ordinal, counts, continuous, and/or skewed continuous, and methods for modeling various graphs including multiplex, multilevel, and multilayer ...
Tamara Dimitrova, Ljupco Kocarev
doaj +1 more source