Results 11 to 20 of about 6,429,693 (361)
Deep Gaussian Mixture Models [PDF]
, 2017 Deep learning is a hierarchical inference method formed by subsequent multiple layers of learning able to more efficiently describe complex relationships. In this work, Deep Gaussian Mixture Models are introduced and discussed.
McLachlan, Geoffrey J., Viroli, Cinzia
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Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs [PDF]
Computer Vision and Pattern Recognition, 2016 Deep learning has achieved a remarkable performance breakthrough in several fields, most notably in speech recognition, natural language processing, and computer vision.
Federico Monti +5 more
semanticscholar +1 more source
Sympathetic cooling route to Bose-Einstein condensate and Fermi-liquid mixtures [PDF]
, 2005 We discuss a sympathetic cooling strategy that can successfully mitigate fermion-hole heating in a dilute atomic Fermi-Bose mixture and access the temperature regime in which the fermions behave as a Fermi liquid.
B. M. Smirnov +5 more
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Yield and Quality Features of Buckwheat-Soybean Mixtures in Organic Agricultural Conditions
Turkish Journal of Agriculture: Food Science and Technology, 2017 This study was carried out during the summer of 2014 to determine alternative quality forage sources that could be grown in the Aydın ecological conditions.
Mustafa Sürmen, Emre Kara
doaj +1 more source
Molecules, 2023 In many industrial fields, in medicine or pharmacy, there are used multi-component mixtures of surfactants as well as more and more often mixtures containing biosurfactants.
Anna Zdziennicka +5 more
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Complex Mixtures: Array PBPK Modeling of Jet Fuel Components
Toxics, 2023 An array physiologically-based pharmacokinetic (PBPK) model represents a streamlined method to simultaneously quantify dosimetry of multiple compounds. To predict internal dosimetry of jet fuel components simultaneously, an array PBPK model was coded to ...
Teresa R. Sterner +2 more
doaj +1 more source
Journal of Mathematical Physics, 2002 An index evaluating the amount of chirality of a mixture of colored random variables is defined. Properties are established. Extreme chiral mixtures are characterized and examples are given. Connections between chirality, Wasserstein distances, and least squares Procrustes methods are pointed out.
openaire +3 more sources
Semiparametric mixture: Continuous scale mixture approach [PDF]
Computational Statistics & Data Analysis, 2016 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Xiang, Sijia, Yao, Weixin, Seo, Byungtae
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Multivariate normal mixture GARCH [PDF]
, 2006 We present a multivariate generalization of the mixed normal GARCH model proposed in Haas, Mittnik, and Paolella (2004a). Issues of parametrization and estimation are discussed.
Haas, Markus +2 more
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Identifying Mixtures of Mixtures Using Bayesian Estimation [PDF]
Journal of Computational and Graphical Statistics, 2017 The use of a finite mixture of normal distributions in model-based clustering allows to capture non-Gaussian data clusters. However, identifying the clusters from the normal components is challenging and in general either achieved by imposing constraints on the model or by using post-processing procedures.
Malsiner-Walli, Gertraud +2 more
openaire +4 more sources