Results 71 to 80 of about 129,562 (262)
Computer Assisted Methods in Engineering and ScienceThis paper describes the application of particle swarm optimization (PSO) for the hyperparameter optimization problem of multi-layered perceptron (MLP) model.
Kenta Shiomi, Tetsuya Sato, Eisuke Kita
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A Statistical Approach to Provide Explainable Convolutional Neural Network Parameter Optimization
International Journal of Computational Intelligence Systems, 2019 Algorithms based on convolutional neural networks (CNNs) have been great attention in image processing due to their ability to find patterns and recognize objects in a wide range of scientific and industrial applications.
Saman Akbarzadeh +2 more
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LSTM Hyperparameters optimization with Hparam parameters for Bitcoin Price Prediction
Sakarya University Journal of Computer and Information Sciences, 2023 Machine learning and deep learning algorithms produce very different results with different examples of their hyperparameters. Algorithm parameters require optimization because they aren't specific for all problems.
Fatih Akay, I.sibel Kervancı
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Compositional optimization of hard-magnetic phases with machine-learning models
, 2018 Machine Learning (ML) plays an increasingly important role in the discovery and design of new materials. In this paper, we demonstrate the potential of ML for materials research using hard-magnetic phases as an illustrative case. We build kernel-based ML
Elsässer, Christian +4 more
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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
EigenGP: Gaussian Process Models with Adaptive Eigenfunctions [PDF]
, 2015 Gaussian processes (GPs) provide a nonparametric representation of functions. However, classical GP inference suffers from high computational cost for big data. In this paper, we propose a new Bayesian approach, EigenGP, that learns both basis dictionary
Peng, Hao, Qi, Yuan
core
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
A local Bayesian optimizer for atomic structures
, 2019 A local optimization method based on Bayesian Gaussian Processes is developed and applied to atomic structures. The method is applied to a variety of systems including molecules, clusters, bulk materials, and molecules at surfaces.
del Río, Estefanía Garijo +2 more
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Advanced Materials, EarlyView.Magnetic tunnel junctions (MTJs) using MgO tunnel barriers face challenges of high resistance‐area product and low tunnel magnetoresistance (TMR). To discover alternative materials, Literature Enhanced Ab initio Discovery (LEAD) is developed. The LEAD‐predicted materials are theoretically evaluated, showing that MTJs with dusting of ScN or TiN on ...
Sabiq Islam +6 more
wiley +1 more source
MathematicsThis paper introduces a novel hyperparameter optimization framework for regression tasks called the Combined-Sampling Algorithm to Search the Optimized Hyperparameters (CASOH).
Nguyen Huu Tiep +8 more
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