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Practical and theoretical aspects of mixture‐of‐experts modeling: An overview
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Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/0471725293openalex-snapshot
Discriminant Analysis and Statistical Pattern Recognition
inferredcites →https://doi.org/10.1002/cjs.10083openalex-snapshot
New estimation and feature selection methods in mixture‐of‐experts models
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Modeling nonlinear time series with local mixtures of generalized linear models
inferredcites →https://doi.org/10.1002/widm.1198openalex-snapshot
An introduction to Majorization‐Minimization algorithms for machine learning and statistical estimation
inferredcites →https://doi.org/10.1007/978-1-4419-9634-3openalex-snapshot
Probability for Statistics and Machine Learning: Fundamentals and Advanced Topics
inferredcites →https://doi.org/10.1007/978-1-4614-4818-1openalex-snapshot
Essential statistical inference : theory and methods
inferredcites →https://doi.org/10.1007/978-3-540-48503-2openalex-snapshot
Concentration Inequalities and Model Selection
inferredcites →https://doi.org/10.1007/bf00049423openalex-snapshot
Monotonicity of quadratic-approximation algorithms
inferredcites →https://doi.org/10.1007/bf02551274openalex-snapshot
Approximation by superpositions of a sigmoidal function
inferredcites →https://doi.org/10.1007/bfb0020170openalex-snapshot
Global least-squares vs. EM training for the Gaussian mixture of experts
inferredcites →https://doi.org/10.1007/s00357-012-9114-3openalex-snapshot
Local Statistical Modeling via a Cluster-Weighted Approach with Elliptical Distributions
inferredcites →https://doi.org/10.1007/s10462-012-9338-yopenalex-snapshot
Mixture of experts: a literature survey
inferredcites →https://doi.org/10.1007/s11222-014-9461-5openalex-snapshot
High-dimensional regression with gaussian mixtures and partially-latent response variables
inferredcites →https://doi.org/10.1007/s11634-011-0096-5openalex-snapshot
Model-based clustering and segmentation of time series with changes in regime
inferredcites →https://doi.org/10.1007/s11634-015-0209-7openalex-snapshot
Maximum likelihood estimation of Gaussian mixture models without matrix operations
inferredcites →https://doi.org/10.1007/s11749-010-0197-zopenalex-snapshot
ℓ1-penalization for mixture regression models
inferredcites →https://doi.org/10.1007/s40815-016-0285-7openalex-snapshot
Diagnosing the ADHD Using a Mixture of Expert Fuzzy Models
inferredcites →https://doi.org/10.1016/j.artmed.2007.06.001openalex-snapshot
Extension of mixture-of-experts networks for binary classification of hierarchical data
inferredcites →https://doi.org/10.1016/j.ast.2015.02.019openalex-snapshot
Surrogate models and mixtures of experts in aerodynamic performance prediction for aircraft mission analysis
inferredcites →https://doi.org/10.1016/j.csda.2006.03.002openalex-snapshot
Multivariate time series modeling and classification via hierarchical VAR mixtures
inferredcites →https://doi.org/10.1016/j.csda.2006.08.014openalex-snapshot
Fitting finite mixtures of generalized linear regressions in R
inferredcites →https://doi.org/10.1016/j.csda.2006.09.032openalex-snapshot
Modelling nonlinear count time series with local mixtures of Poisson autoregressions
inferredcites →https://doi.org/10.1016/j.csda.2012.12.007openalex-snapshot
Mixture models for clustering multilevel growth trajectories
inferredcites →https://doi.org/10.1016/j.csda.2014.10.016openalex-snapshot
Laplace mixture of linear experts
inferredcites →https://doi.org/10.1016/j.ins.2014.01.008openalex-snapshot
Embedded local feature selection within mixture of experts
inferredcites →https://doi.org/10.1016/j.jeconom.2016.02.012openalex-snapshot
Gaussian mixture vector autoregression
inferredcites →https://doi.org/10.1016/j.jmva.2016.02.001openalex-snapshot
Simultaneous variable selection and de-coarsening in multi-path change-point models
inferredcites →https://doi.org/10.1016/j.neucom.2009.12.023openalex-snapshot
A hidden process regression model for functional data description. Application to curve discrimination
inferredcites →https://doi.org/10.1016/j.neucom.2010.12.007openalex-snapshot
Asymptotic properties of mixture-of-experts models
inferredcites →https://doi.org/10.1016/j.neucom.2012.10.030openalex-snapshot
Model-based functional mixture discriminant analysis with hidden process regression for curve classification
inferredcites →https://doi.org/10.1016/j.neucom.2013.05.044openalex-snapshot
Activity recognition with android phone using mixture-of-experts co-trained with labeled and unlabeled data
inferredcites →https://doi.org/10.1016/j.neucom.2017.05.044openalex-snapshot
Skew t mixture of experts
inferredcites →https://doi.org/10.1016/j.neunet.2009.06.040openalex-snapshot
Time series modeling by a regression approach based on a latent process
inferredcites →https://doi.org/10.1016/j.neunet.2016.03.002openalex-snapshot
Robust mixture of experts modeling using the t distribution
inferredcites →https://doi.org/10.1016/j.neuroimage.2015.10.045openalex-snapshot
Capturing heterogeneous group differences using mixture-of-experts: Application to a study of aging
inferredcites →https://doi.org/10.1016/j.patrec.2005.12.002openalex-snapshot
A regularized minimum cross-entropy algorithm on mixtures of experts for time series prediction and curve detection
inferredcites →https://doi.org/10.1016/j.patrec.2013.09.022openalex-snapshot
Multi-sensor background subtraction by fusing multiple region-based probabilistic classifiers
inferredcites →https://doi.org/10.1016/s0022-0000(76)80021-9openalex-snapshot
Sufficient conditions for the convergence of monotonic mathematicalprogramming algorithms
inferredcites →https://doi.org/10.1016/s0893-6080(99)00043-xopenalex-snapshot
Improved learning algorithms for mixture of experts in multiclass classification
inferredcites →https://doi.org/10.1016/s0893-6080(99)00066-0openalex-snapshot
On the identifiability of mixtures-of-experts
inferredcites →https://doi.org/10.1017/s026646661300042xopenalex-snapshot
POSTERIOR CONSISTENCY IN CONDITIONAL DENSITY ESTIMATION BY COVARIATE DEPENDENT MIXTURES
inferredcites →https://doi.org/10.1049/iet-cvi.2016.0138openalex-snapshot
Context‐based classification via mixture of hidden Markov model experts with applications in landmine detection
inferredcites →https://doi.org/10.1080/01621459.1999.10474189openalex-snapshot
A Test for Global Maximum
inferredcites →https://doi.org/10.1080/07474938.2010.481987openalex-snapshot
Time Series Mixtures of GeneralizedtExperts: ML Estimation and an Application to Stock Return Density Forecasting
inferredcites →https://doi.org/10.1109/18.669150openalex-snapshot
Error bounds for functional approximation and estimation using mixtures of experts
inferredcites →https://doi.org/10.1109/18.841177openalex-snapshot
On the asymptotic normality of hierarchical mixtures-of-experts for generalized linear models
inferredcites →https://doi.org/10.1109/34.865189openalex-snapshot
Assessing a mixture model for clustering with the integrated completed likelihood
inferredcites →https://doi.org/10.1109/72.80265openalex-snapshot
The Stone-Weierstrass theorem and its application to neural networks
inferredcites →https://doi.org/10.1109/icassp.2015.7178776openalex-snapshot
A Gaussian Mixture Model layer jointly optimized with discriminative features within a Deep Neural Network architecture
inferredcites →https://doi.org/10.1109/ssp.2014.6884624openalex-snapshot
Asymptotic inference for hidden process regression models
inferredcites →https://doi.org/10.1109/tac.1974.1100705openalex-snapshot
A new look at the statistical model identification
inferredcites →https://doi.org/10.1109/tbme.2014.2377695openalex-snapshot
Monitoring Cardiac Stress Using Features Extracted From S1 Heart Sounds
inferredcites →https://doi.org/10.1109/tcyb.2017.2734043openalex-snapshot
Stochastic Configuration Networks: Fundamentals and Algorithms
inferredcites →https://doi.org/10.1109/tnn.2004.826217openalex-snapshot
Using the EM Algorithm to Train Neural Networks: Misconceptions and a New Algorithm for Multiclass Classification
inferredcites →https://doi.org/10.1109/tnn.2004.839356openalex-snapshot
Mixtures-of-Experts of Autoregressive Time Series: Asymptotic Normality and Model Specification
inferredcites →https://doi.org/10.1109/tnnls.2012.2200299openalex-snapshot
Twenty Years of Mixture of Experts
inferredcites →https://doi.org/10.1111/biom.12020openalex-snapshot
Regularization in Finite Mixture of Regression Models with Diverging Number of Parameters
inferredcites →https://doi.org/10.1111/j.1467-842x.1992.tb01356.xopenalex-snapshot
FITTING FINITE MIXTURE MODELS IN A REGRESSION CONTEXT
inferredcites →https://doi.org/10.1111/j.2517-6161.1996.tb02080.xopenalex-snapshot
Regression Shrinkage and Selection Via the Lasso
inferredcites →https://doi.org/10.1111/jtsa.12108openalex-snapshot
A Gaussian Mixture Autoregressive Model for Univariate Time Series
inferredcites →https://doi.org/10.1137/120891009openalex-snapshot
A Unified Convergence Analysis of Block Successive Minimization Methods for Nonsmooth Optimization
inferredcites →https://doi.org/10.1145/2813885.2737999openalex-snapshot
Celebrating diversity: a mixture of experts approach for runtime mapping in dynamic environments
inferredcites →https://doi.org/10.1162/neco.1991.3.1.79openalex-snapshot
Adaptive Mixtures of Local Experts
inferredcites →https://doi.org/10.1162/neco.1994.6.2.181openalex-snapshot
Hierarchical Mixtures of Experts and the EM Algorithm
inferredcites →https://doi.org/10.1162/neco_a_00354openalex-snapshot
On Convergence Rates of Mixtures of Polynomial Experts
inferredcites →https://doi.org/10.1162/neco_a_00892openalex-snapshot
A Universal Approximation Theorem for Mixture-of-Experts Models
inferredcites →https://doi.org/10.1198/016214501753382273openalex-snapshot
Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
inferredcites →https://doi.org/10.1198/016214507000000590openalex-snapshot
Variable Selection in Finite Mixture of Regression Models
inferredcites →https://doi.org/10.1198/jcgs.2010.09014openalex-snapshot
MM Algorithms for Some Discrete Multivariate Distributions
inferredcites →https://doi.org/10.1214/14-ejs939openalex-snapshot
Mixture of Gaussian regressions model with logistic weights, a penalized maximum likelihood approach
inferredcites →https://doi.org/10.1214/15-ejs1026openalex-snapshot
Estimation and model selection for model-based clustering with the conditional classification likelihood
inferredcites →https://doi.org/10.1214/aoms/1177697731openalex-snapshot
Asymptotic Properties of Non-Linear Least Squares Estimators
inferredcites →https://doi.org/10.1214/aoms/1177729694openalex-snapshot
On Information and Sufficiency
inferredcites →https://doi.org/10.1214/aos/1018031265openalex-snapshot
Hierarchical mixtures-of-experts for exponential family regression models: approximation and maximum likelihood estimation
inferredcites →https://doi.org/10.1214/aos/1176344136openalex-snapshot
Estimating the Dimension of a Model
inferredcites →https://doi.org/10.18637/jss.v028.i04openalex-snapshot
FlexMix Version 2: Finite Mixtures with Concomitant Variables and Varying and Constant Parameters
inferredcites →https://doi.org/10.2307/1912526openalex-snapshot
Maximum Likelihood Estimation of Misspecified Models
inferredcites →https://doi.org/10.2307/2348768openalex-snapshot
On the Choice of Starting Values for the EM Algorithm in Fitting Mixture Models
inferredcites →https://doi.org/10.2307/3315017openalex-snapshot
Vec and vech operators for matrices, with some uses in jacobians and multivariate statistics
inferredcites →https://doi.org/10.2516/ogst/2014013openalex-snapshot
Unsupervised Segmentation of Spectral Images with a Spatialized Gaussian Mixture Model and Model Selection
inferredcites →https://doi.org/10.32614/r.manualsopenalex-snapshot
R: A Language and Environment for Statistical Computing
inferredcites →https://doi.org/10.3389/fncom.2014.00173openalex-snapshot
Gaussian mixture models and semantic gating improve reconstructions from human brain activity
inferredcites →https://doi.org/10.3389/fnhum.2014.00971openalex-snapshot
Anatomical and functional plasticity in early blind individuals and the mixture of experts architecture
inferredcites →https://doi.org/10.48550/arxiv.1506.03478openalex-snapshot
Generative Image Modeling Using Spatial LSTMs
inferredcites →https://doi.org/10.48550/arxiv.1609.05559openalex-snapshot
Opponent Modeling in Deep Reinforcement Learning
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Regularized Estimation and Feature Selection in Mixtures of Gaussian-Gated Experts Models
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Ant Colony Sampling with Mixture of Experts for Combinatorial Optimization
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A mixture of experts regression model for functional response with functional covariates
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Estimation and group-feature selection in sparse mixture-of-experts with diverging number of parameters
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Mixture of experts models for multilevel data: Modeling framework and approximation theory
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Bayesian nonparametric mixture of experts for inverse problems
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Leveraging multimodal large language model chatbots in oral radiology: a comprehensive evaluation using questions from a Korean dental university
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Identifying Brexit voting patterns in the British house of commons: an analysis based on Bayesian mixture models with flexible concomitant covariate effects
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