Results 81 to 90 of about 471,788 (278)
Mathematical and Computational Applications, 2020 We present a novel bi-objective approach to address the data-driven learning problem of Bayesian networks. Both the log-likelihood and the complexity of each candidate Bayesian network are considered as objectives to be optimized by our proposed ...
Vicente-Josué Aguilera-Rueda +2 more
doaj +1 more source
Superionic Amorphous Li2ZrCl6 and Li2HfCl6
Advanced Materials, EarlyView.Amorphous Li2HfCl6 and L2ZrCl6 are shown to be promising solid‐state electrolytes with predicted ionic conductivities >20 mS·cm−1. Molecular dynamics simulations with machine‐learning force fields reveal that anion vibrations and flexible MCl6 octahedra soften the Li coordination cage and enhance mobility. Correlation between Li‐ion diffusivity and the
Shukai Yao, De‐en Jiang
wiley +1 more source
Bayesian Learning of Kernel Embeddings [PDF]
, 2016 Conference paper appearing in UAI 2016, including ...
Filippi, S +3 more
openaire +3 more sources
Advanced Materials, EarlyView.The perspective presents an integrated view of neuromorphic technologies, from device physics to real‐time applicability, while highlighting the necessity of full‐stack co‐optimization. By outlining practical hardware‐level strategies to exploit device behavior and mitigate non‐idealities, it shows pathways for building efficient, scalable, and ...
Kapil Bhardwaj +8 more
wiley +1 more source
Bayesian Neural Networks via MCMC: A Python-Based Tutorial
IEEE AccessBayesian inference provides a methodology for parameter estimation and uncertainty quantification in machine learning and deep learning methods. Variational inference and Markov Chain Monte-Carlo (MCMC) sampling methods are used to implement Bayesian ...
Rohitash Chandra, Joshua Simmons
doaj +1 more source
Bayesian Learning of Sum-Product Networks
, 2019 Sum-product networks (SPNs) are flexible density estimators and have received significant attention due to their attractive inference properties. While parameter learning in SPNs is well developed, structure learning leaves something to be desired: Even ...
Ge, Hong +4 more
core
Nanomaterial Integration at Liquid–Liquid Interfaces for Green Catalysis
Advanced Materials, EarlyView.Functional nanomaterials assembled at liquid–liquid interfaces create dual‐role platforms serving as emulsion stabilizers and catalytic sites, offering enhanced reaction kinetics with improved catalyst recovery and recyclability. This review examines design strategies, structure‐performance relationships, and industrial implementation prospects of ...
Bokgi Seo +6 more
wiley +1 more source
Bayesian Semi-supervised Learning with Graph Gaussian Processes [PDF]
, 2018 We propose a data-efficient Gaussian process-based Bayesian approach to the semi-supervised learning problem on graphs. The proposed model shows extremely competitive performance when compared to the state-of-the-art graph neural networks on semi ...
Colombo, Nicolo +2 more
core +1 more source
Magnetic Textiles: A Review of Materials, Fabrication, Properties, and Applications
Advanced Materials Technologies, EarlyView.Magnetic textiles (M‐textiles) are emerging as a programmable materials platform that merges magnetic matter with hierarchical textile structures. This article consolidates magnetic material classes, textile architectures, and fabrication and magnetization strategies, revealing structure–property–function relationships that govern magneto‐mechanical ...
Li Ke +3 more
wiley +1 more source
HeliyonA Bayesian method based on the learning rate parameter η is called a generalized Bayesian method. In this study, joint hybrid censored type I and type II samples from k exponential populations were examined to determine the influence of the parameter η ...
Yahia Abdel-Aty +2 more
doaj +1 more source