Results 51 to 60 of about 131,851 (273)
Machine Learning‐Assisted Inverse Design of Soft and Multifunctional Hybrid Liquid Metal Composites
Advanced Functional Materials, EarlyView.A machine learning framework is presented for inverse design of synthesizable multifunctional composites containing both liquid metal and solid inclusions. By integrating physics‐based modeling, data‐driven prediction, and Bayesian optimization, the approach enables intelligent design of experiments to identify optimal compositions and realize these ...
Lijun Zhou +5 more
wiley +1 more source
A Hybrid Sparrow Search Algorithm of the Hyperparameter Optimization in Deep Learning
Mathematics, 2022 Deep learning has been widely used in different fields such as computer vision and speech processing. The performance of deep learning algorithms is greatly affected by their hyperparameters.
Yanyan Fan +5 more
doaj +1 more source
Hyperparameter Optimization via Sequential Uniform Designs
, 2020 Hyperparameter optimization (HPO) plays a central role in the automated machine learning (AutoML). It is a challenging task as the response surfaces of hyperparameters are generally unknown, hence essentially a global optimization problem. This paper reformulates HPO as a computer experiment and proposes a novel sequential uniform design (SeqUD ...
Yang, Zebin, Zhang, Aijun
openaire +3 more sources
Unveiling the Role of Curvature in Carbon for Improved Energy Release of Ammonium Perchlorate
Advanced Materials, EarlyView.High‐curvature carbon materials identified via machine learning and simulation can enhance the heat release and combustion performance of ammonium perchlorate. ABSTRACT The catalytic role of carbon curvature in the thermal decomposition of ammonium perchlorate (AP) remains largely unexplored. To address this gap, this study employs machine learning and
Dan Liu +8 more
wiley +1 more source
IEEE Access, 2019 Heart failure is considered one of the leading cause of death around the world. The diagnosis of heart failure is a challenging task especially in under-developed and developing countries where there is a paucity of human experts and equipments.
Ashir Javeed +5 more
doaj +1 more source
Understanding and Comparing Scalable Gaussian Process Regression for Big Data
, 2018 As a non-parametric Bayesian model which produces informative predictive distribution, Gaussian process (GP) has been widely used in various fields, like regression, classification and optimization.
Cai, Jianfei +3 more
core +1 more source
Advanced Materials, EarlyView.AI‐Assisted Workflow for (Scanning) Transmission Electron Microscopy: From Data Analysis Automation to Materials Knowledge Unveiling. Abstract (Scanning) transmission electron microscopy ((S)TEM) has significantly advanced materials science but faces challenges in correlating precise atomic structure information with the functional properties of ...
Marc Botifoll +19 more
wiley +1 more source
Design of adaptive soft sensor based on Bayesian optimization
Case Studies in Chemical and Environmental Engineering, 2022 When adaptive soft sensors are introduced to industrial plants, an appropriate combination of the type of adaptation mechanism, hyperparameters of the mechanism, regression model, and hyperparameters of the model must be selected for predictive soft ...
Shuto Yamakage, Hiromasa Kaneko
doaj +1 more source
Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms [PDF]
, 2012 Many different machine learning algorithms exist; taking into account each algorithm's hyperparameters, there is a staggeringly large number of possible alternatives overall.
Hoos, Holger H. +3 more
core +2 more sources
Can We Discover Double Higgs Production at the LHC?
, 2017 We explore double Higgs production via gluon fusion in the $b\bar{b} \gamma \gamma $ channel at the high-luminosity LHC using machine learning tools. We first propose a Bayesian optimization approach to select cuts on kinematic variables, obtaining a $30-
Alves, Alexandre +2 more
core +1 more source