Results 161 to 170 of about 471,788 (278)

Adaptive Bayesian learning for stability characterization of re-entry vehicles. [PDF]

Sci Rep
Tiwari B, Musharrat L, Romeo SAS, Kassem A, Kara K, San O.   +5 more
europepmc   +1 more source

DOA Estimation for Massive MIMO Systems with Unknown Mutual Coupling Based on Block Sparse Bayesian Learning. [PDF]

Sensors (Basel), 2022
Liu Y, Dong N, Zhang X, Zhao X, Zhang Y, Qiu T.   +5 more
europepmc   +1 more source

A Machine Learning Model for Interpretable PECVD Deposition Rate Prediction

Advanced Intelligent Discovery, EarlyView.
This study develops six machine learning models (k‐nearest neighbors, support vector regression, decision tree, random forest, CatBoost, and backpropagation neural network) to predict SiNx deposition rates in plasma‐enhanced chemical vapor deposition using hybrid production and simulation data.
Yuxuan Zhai, Wenrui Wang, Rui Chen, Tong Yu, Xiaojuan Zheng, Hua Shao, Dashan Shang, Ling Li, Lado Filipovic   +8 more
wiley   +1 more source

Integrated single marker scanning and sparse Bayesian learning improves performance of detection for GWAS. [PDF]

Plant Methods
Zhang J, Wu Z, Cai M, Liu K, Han X, Liu C, Han G, Wen Y.   +7 more
europepmc   +1 more source

Neural surprise in somatosensory Bayesian learning. [PDF]

PLoS Comput Biol, 2021
Gijsen S, Grundei M, Lange RT, Ostwald D, Blankenburg F.   +4 more
europepmc   +1 more source

Topology‐Aware Machine Learning for High‐Throughput Screening of MOFs in C8 Aromatic Separation

Advanced Intelligent Discovery, EarlyView.
We screened 15,335 Computation‐Ready, Experimental Metal–Organic Frameworks (CoRE‐MOFs) using a topology‐aware machine learning (ML) model that integrates structural, chemical, pore‐size, and topological descriptors. Top‐performing MOFs exhibit aromatic‐enriched cavities and open metal sites that enable π–π and C–H···π interactions, serving as ...
Yu Li, Honglin Li, Jialu Li, Wan‐Lu Li
wiley   +1 more source

BAYESIAN LEARNING OF CLINICALLY MEANINGFUL SEPSIS PHENOTYPES IN NORTHERN TANZANIA. [PDF]

Ann Appl Stat
Dombowsky A, Dunson DB, Madut DB, Rubach MP, Herring AH.   +4 more
europepmc   +1 more source

Shaky Student Growth? A Comparison of Robust Bayesian Learning Progress Estimation Methods. [PDF]

J Intell, 2022
Forthmann B, Förster N, Souvignier E.
europepmc   +1 more source

Deep Learning‐Assisted Design of Mechanical Metamaterials

Advanced Intelligent Discovery, EarlyView.
This review examines the role of data‐driven deep learning methodologies in advancing mechanical metamaterial design, focusing on the specific methodologies, applications, challenges, and outlooks of this field. Mechanical metamaterials (MMs), characterized by their extraordinary mechanical behaviors derived from architected microstructures, have ...
Zisheng Zong, Pu Wen, Zhiping Chai, Dunxuan Wu, Han Ding, Zhigang Wu   +5 more
wiley   +1 more source

Hierarchical sparse Bayesian learning with adaptive Laplacian prior for single image super-resolution. [PDF]

Sci Rep
Qi M, Zhou Y, Hu Y, Xie C, Xu S.
europepmc   +1 more source