Výsledky vyhledávání - "EXPECTATION-maximization algorithms"

A GAMP-Based Low Complexity Sparse Bayesian Learning Algorithm Autor Al-Shoukairi, Maher, Schniter, Philip, Rao, Bhaskar D.

“…In this paper, we present an algorithm for the sparse signal recovery problem that incorporates damped Gaussian generalized approximate message passing (GGAMP)…”
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Weighted and Class-Specific Maximum Mean Discrepancy for Unsupervised Domain Adaptation Autor Yan, Hongliang, Li, Zhetao, Wang, Qilong, Li, Peihua, Xu, Yong, Zuo, Wangmeng

“…Although maximum mean discrepancy (MMD) has achieved great success in unsupervised domain adaptation (UDA), most of existing UDA methods ignore the issue of…”
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Expectation-Maximization Gaussian-Mixture Approximate Message Passing Autor Vila, Jeremy P., Schniter, Philip

“…When recovering a sparse signal from noisy compressive linear measurements, the distribution of the signal's non-zero coefficients can have a profound effect…”
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Learning the Dynamics of Arterial Traffic From Probe Data Using a Dynamic Bayesian Network Autor Hofleitner, A., Herring, R., Abbeel, P., Bayen, A.

“…Estimating and predicting traffic conditions in arterial networks using probe data has proven to be a substantial challenge. Sparse probe data represent the…”
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Covariance-Free Sparse Bayesian Learning Autor Lin, Alexander, Song, Andrew H., Bilgic, Berkin, Ba, Demba

“…Sparse Bayesian learning (SBL) is a powerful framework for tackling the sparse coding problem while also providing uncertainty quantification. The most popular…”
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Dynamic Probabilistic Predictable Feature Analysis for Multivariate Temporal Process Monitoring Autor Fan, Wei, Zhu, Qinqin, Ren, Shaojun, Zhang, Liang, Si, Fengqi

“…Dynamic statistical process monitoring methods have been widely studied and applied in modern industrial processes. These methods aim to extract the most…”
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Adaptive State Estimation for Power Systems Measured by PMUs With Unknown and Time-Varying Error Statistics Autor Cheng, Gang, Lin, Yuzhang, Chen, Yanbo, Bi, Tianshu

“…Measurement error is a crucial factor that determines the accuracy of state estimation (SE). Conventional estimators have fixed models, and can yield optimal…”
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Asymptotic errors for teacher-student convex generalized linear models (or: how to prove Kabashima's replica formula) Autor Gerbelot, Cedric, Abbara, Alia, Krzakala, Florent

“…There has been a recent surge of interest in the study of asymptotic reconstruction performance in various cases of generalized linear estimation problems in…”
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Confirmation Bias in Gaussian Mixture Models Autor Balanov, Amnon, Bendory, Tamir, Huleihel, Wasim

“…Confirmation bias, the tendency to interpret information in a way that aligns with one's preconceptions, can profoundly impact scientific research, leading to…”
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2D Segmentation Using a Robust Active Shape Model With the EM Algorithm Autor Santiago, Carlos, Nascimento, Jacinto C., Marques, Jorge S.

“…Statistical shape models have been extensively used in a wide range of applications due to their effectiveness in providing prior shape information for object…”
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Performance Comparison of Software Reliability Estimation Algorithms Autor Yano, Hiromu, Dohi, Tadashi, Okamura, Hiroyuki

“…Specific optimization algorithms have been developed for the purpose of automated software reliability assessment tools. In this article, we propose the Monte…”
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Dynamic Compressive Sensing of Time-Varying Signals Via Approximate Message Passing Autor Ziniel, Justin, Schniter, Philip

“…In this work the dynamic compressive sensing (CS) problem of recovering sparse, correlated, time-varying signals from sub-Nyquist, non-adaptive, linear…”
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An Improved Power Expectation Propagation Detector for Massive MIMO Systems Autor Tan, Xiaosi, Ding, Mingyuan, Ji, Zhenhao, Zhang, Zaichen, You, Xiaohu, Zhang, Chuan

“…Expectation propagation (EP) achieves promising performance under various antenna configurations and modulations in massive multiple-input multiple-output…”
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An Improved Compound Gaussian Model for Bivariate Surface EMG Signals Related to Strength Training Autor Kusuru, Durgesh, Turlapaty, Anish C., Thakur, Mainak

“…Recent literature suggests that the surface electromyography (sEMG) signals have nonstationary statistical characteristics, specifically due to the random…”
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Model-Based Clustering of Mixed Data With Sparse Dependence Autor Choi, Young-Geun, Ahn, Soohyun, Kim, Jayoun

“…Mixed data refers to a mixture of continuous and categorical variables. The clustering problem with mixed data is a long-standing statistical problem. The…”
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Efficient High-Dimensional Inference in the Multiple Measurement Vector Problem Autor Ziniel, J., Schniter, P.

“…In this work, a Bayesian approximate message passing algorithm is proposed for solving the multiple measurement vector (MMV) problem in compressive sensing, in…”
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Gaussian Mixture Model Uncertainty Modeling for Power Systems Considering Mutual Assistance of Latent Variables Autor Yang, Xiao, Li, Yuanzheng, Zhao, Yong, Li, Yang, Hao, Guokai, Wang, Yan-Wu

“…Gaussian mixture model (GMM) and Dirichlet process mixture model (DPMM) are the primary techniques used to characterize uncertainties in power systems, which…”
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The Path Inference Filter: Model-Based Low-Latency Map Matching of Probe Vehicle Data Autor Hunter, Timothy, Abbeel, Pieter, Bayen, Alexandre

“…We consider the problem of reconstructing vehicle trajectories from sparse sequences of GPS points, for which the sampling interval is between 1 s and 2 min…”
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Multirate Dynamic Process Monitoring Based on Multirate Linear Gaussian State-Space Model Autor Cong, Ya, Zhou, Le, Song, Zhihuan, Ge, Zhiqiang

“…Multivariate statistical process monitoring (MSPM) has been widely used in modern industries and most of traditional MSPM methods are developed using uniformly…”
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Model-Based Expectation-Maximization Source Separation and Localization Autor Mandel, M.I., Weiss, R.J., Ellis, D.

“…This paper describes a system, referred to as model-based expectation-maximization source separation and localization (MESSL), for separating and localizing…”
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