Results 61 to 70 of about 471,788 (278)
A Workflow to Accelerate Microstructure‐Sensitive Fatigue Life Predictions
Advanced Engineering Materials, EarlyView.This study introduces a workflow to accelerate predictions of microstructure‐sensitive fatigue life. Results from frameworks with varying levels of simplification are benchmarked against published reference results. The analysis reveals a trade‐off between accuracy and model complexity, offering researchers a practical guide for selecting the optimal ...
Luca Loiodice +2 more
wiley +1 more source
Relevance Vector Machines for Enhanced BER Probability in DMT-Based Systems
Journal of Electrical and Computer Engineering, 2010 A new channel estimation method for discrete multitone (DMT) communication system based on sparse Bayesian learning relevance vector machine (RVM) method is presented.
Ashraf A. Tahat, Nikolaos P. Galatsanos
doaj +1 more source
International Journal of Interactive Multimedia and Artificial Intelligence, 2023 Bayesian networks are analytical models that may represent probabilistic dependent connections among variables and are useful in machine learning for generating knowledge structure.
Hoshang Qasim Awla +2 more
doaj +1 more source
Additive Gaussian Process Regression for Predictive Design of High‐Performance, Printable Silicones
Advanced Engineering Materials, EarlyView.A chemistry‐aware design framework for tuning printable polydimethylsiloxane (PDMS) for vat photopolymerization (VPP) is developed using additive Gaussian process (GP) modeling. Polymer network mechanics informs variable groupings, feasible formulation constraints, and interaction variables.
Roxana Carbonell +3 more
wiley +1 more source
On Non-Bayesian Social Learning [PDF]
, 2011 We study a model of information aggregation and social learning recently proposed by Jadbabaie, Sandroni, and Tahbaz-Salehi, in which individual agents try to learn a correct state of the world by iteratively updating their beliefs using private ...
Jadbabaie, Ali, Molavi, Pooya
core
Leveraging Crowdsourcing Data For Deep Active Learning - An Application: Learning Intents in Alexa
, 2018 This paper presents a generic Bayesian framework that enables any deep learning model to actively learn from targeted crowds. Our framework inherits from recent advances in Bayesian deep learning, and extends existing work by considering the targeted ...
Damianou, Andreas +3 more
core +1 more source
Learning in monotone bayesian games [PDF]
Journal of Dynamics and Games, 2015 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire +3 more sources
Multimodal Data‐Driven Microstructure Characterization
Advanced Engineering Materials, EarlyView.A self‐consistent autonomous workflow for EBSP‐based microstructure segmentation by integrating PCA, GMM clustering, and cNMF with information‐theoretic parameter selection, requiring no user input. An optimal ROI size related to characteristic grain size is identified.
Qi Zhang +4 more
wiley +1 more source
Testing students' e-learning via Facebook through Bayesian structural equation modeling.
PLoS ONE, 2017 Learning is an intentional activity, with several factors affecting students' intention to use new learning technology. Researchers have investigated technology acceptance in different contexts by developing various theories/models and testing them by a ...
Hashem Salarzadeh Jenatabadi +4 more
doaj +1 more source
An Experimental High‐Throughput Approach for the Screening of Hard Magnet Materials
Advanced Engineering Materials, EarlyView.An entire workflow for the high‐throughput characterization and analysis of compositionally graded magnetic films is presented. Characterization protocols, data management tools and data analysis approaches are illustrated with test case Sm(Fe, V)12 based films.
William Rigaut +16 more
wiley +1 more source