Results 31 to 40 of about 3,645 (233)

Large-scale heteroscedastic regression via Gaussian process

, 2020
Heteroscedastic regression considering the varying noises among observations has many applications in the fields, such as machine learning and statistics.
Liu, Haitao, Ong, Yew-Soon, Cai, Jianfei
core   +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, Martha Leach, Hongtao Song, Carolyn Seepersad   +3 more
wiley   +1 more source

Standardized Effect Measures Informing Next‐Generation Strategies for Mechanical Stimulation in Cartilage Tissue Engineering

Advanced Healthcare Materials, EarlyView.
This systematic review quantitatively compares conventional mechanical stimulation strategies in cartilage tissue engineering across 85 heterogeneous in vitro studies. Applying standardized effect measures, meta‐analysis reveals that combined compression and shear loading optimally promotes cartilage matrix development.
Jiaqi K. Shen, Tony B. Huang, Catherine E. Davey, Elias Salzer, Gerjo J. V. M. Van Osch, Sandra J. Shefelbine, Marcus G. Pandy, Kathryn S. Stok   +7 more
wiley   +1 more source

Weaving Intelligence: Thermally Drawn Multimaterial Fibers Toward AI‐Enabled Smart Textiles

Advanced Materials, EarlyView.
Thermally drawn multimaterial fibers are rapidly advancing as intelligent structural units for next‐generation smart textiles. Integrating multimaterial architectures with neuromorphic and spiking‐neural‐network principles enables fabrics that can sense, compute, and adapt autonomously.
Vuong Dinh Trung, Jiaxiang Yi, Hieu Nguyen‐Duc, Tien Nu Hoang Lo, Weili Zhao, Chaoqun Dong, Fabien Sorin, Baris Caglar, Lei Wei, Tung Nguyen‐Dang   +9 more
wiley   +1 more source

Heteroscedastic Uncertainty for Robust Generative Latent Dynamics

, 2022
Learning or identifying dynamics from a sequence of high-dimensional observations is a difficult challenge in many domains, including reinforcement learning and control.
Limoyo, Oliver, Chan, Bryan, Marić, Filip, Mahmood, Rupam, Wagstaff, Brandon, Kelly, Jonathan   +5 more
core   +1 more source

Decoupling Intrinsic Molecular Efficacy From Platform Effects: An Interpretable Machine Learning Framework for Unbiased Perovskite Passivator Discovery

Advanced Science, EarlyView.
This study establishes an interpretable machine learning framework that disentangles the intrinsic molecular efficacy of passivators from experimental platform effects—enabling unbiased, high‐throughput discovery of effective perovskite surface modifiers.
Jing Zhang, Ziyuan Li, Shan Gao, Zhen Zhu, Jing Wang, Xiangmei Duan   +5 more
wiley   +1 more source

A Data‐Driven Inverse Design Methodology for Magnetic Soft Millirobots Navigating in Confined Spaces

Advanced Science, EarlyView.
A data‐efficient inverse design framework automates the optimization of magnetic soft millirobots for confined‐space navigation. Integrating a physics‐based Cosserat rod model with Bayesian optimization efficiently identifies high‐performance geometries.
Ziyu Ren, Hong Wang, Chak Wang Tse, Yi Zheng, Xi Chen, Wenqi Hu   +5 more
wiley   +1 more source

Exact D-optimal designs of experiments for linear multiple regression with heteroscedastic observations

Журнал Белорусского государственного университета: Математика, информатика, 2018
In article the problem of construction of exact D-optimal designs of experiments for linear multiple regression in a case when variance of errors of observations depend on a point in which is made is investigated. The class functions describing change of
Valery P. Kirlitsa
doaj  

Fully Bayesian Inference for Meta-Analytic Deconvolution Using Efron’s Log-Spline Prior

Mathematics
Meta-analytic deconvolution seeks to recover the distribution of true effects from noisy site-specific estimates. While Efron’s log-spline prior provides an elegant empirical Bayes solution with excellent point estimation properties, its plug-in nature ...
JoonHo Lee, Daihe Sui
doaj   +1 more source

Initializing the EM Algorithm for Univariate Gaussian, Multi-Component, Heteroscedastic Mixture Models by Dynamic Programming Partitions

, 2018
Setting initial values of parameters of mixture distributions estimated by using the EM recursive algorithm is very important to the overall quality of estimation.
Michal Marczyk, Piotr Widlak, Joanna Polanska, Andrzej Polanski, Monika Pietrowska   +4 more
core   +1 more source