Results 131 to 140 of about 236,656 (279)
Accelerating Stellar Photometric Distance Estimates with Neural Networks
The Astronomical JournalBuilding on the Bayesian approach to estimating stellar distances from broadband photometry, we show that the computation can be accelerated by about an order of magnitude by using neural networks. Focusing on the case of the ugrizy filter complement for
Karlo Mrakovčić +2 more
doaj +1 more source
Frontiers in Systems BiologyThe recently proposed Chemical Reaction Neural Network (CRNN) discovers chemical reaction pathways from time resolved species concentration data in a deterministic manner. Since the weights and biases of a CRNN are physically interpretable, the CRNN acts
Emily Nieves +3 more
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Hyperparameter Optimization of Bayesian Neural Network Using Bayesian Optimization and Intelligent Feature Engineering for Load Forecasting. [PDF]
Sensors (Basel), 2022 Zulfiqar M +3 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
Scientific ReportsWe constructed an early prediction model for postoperative pulmonary complications after thoracoscopic surgery using machine learning and deep learning algorithms.
Cheng-Mao Zhou +4 more
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IEEE AccessMachine learning has evolved into a potent tool for analysing patterns and making predictions from complex data. In this machine learning era, we employed neural network techniques to estimate the parameters of statistical distributions.
P. T. Amrutha, C. S. Rajitha
doaj +1 more source
Bayesian neural network with pretrained protein embedding enhances prediction accuracy of drug-protein interaction. [PDF]
Bioinformatics, 2021 Kim Q, Ko JH, Kim S, Park N, Jhe W.
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 +5 more
wiley +1 more source
Modeling rapid language learning by distilling Bayesian priors into artificial neural networks
Nature CommunicationsHumans can learn languages from remarkably little experience. Developing computational models that explain this ability has been a major challenge in cognitive science.
R. Thomas McCoy, Thomas L. Griffiths
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Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook
Advanced Intelligent Discovery, EarlyView.Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang +4 more
wiley +1 more source