Results 41 to 50 of about 113,531 (269)

Additive Gaussian Process Regression for Predictive Design of High‐Performance, Printable Silicones

Advanced Engineering Materials, EarlyView.
A chemistry‐aware design framework for tuning printable polydimethylsiloxane (PDMS) for vat photopolymerization (VPP) is developed using additive Gaussian process (GP) modeling. Polymer network mechanics informs variable groupings, feasible formulation constraints, and interaction variables.
Roxana Carbonell, Martha Leach, Hongtao Song, Carolyn Seepersad   +3 more
wiley   +1 more source

Hyperparameter Optimization of Random Forest Algorithm to Enhance Performance Metric Evaluation of 5G Coverage Prediction

Buletin Pos dan Telekomunikasi: Media Komunikasi Ilmiah
Utilizing of 5G technology has become a major focus in the development of more advanced and efficient telecommunications networks. In this context, 5G coverage prediction becomes an important aspect in network planning to ensure optimal user experience ...
Hajiar Yuliana, Iskandar, Hendrawan, Sofyan Basuki, M. Reza Hidayat, Atik Charisma, Hurianti Vidyaningtyas   +6 more
doaj   +1 more source

Circular‐Polarization‐Sensitive Organic Photodetectors with a Chiral Nanopatterned Electrode Inverse‐Designed by Genetic Algorithm

Advanced Functional Materials, EarlyView.
A chiral photodetector capable of selectively distinguishing left‐ and right‐handed circularly polarized light is experimentally demonstrated. The device, which features a nanopatterned electrode inverse‐designed by a genetic algorithm within a metal–dielectric–metal nanocavity that incorporates a vacuum‐deposited small‐molecule multilayer, exhibits ...
Kyung Ryoul Park, Jewoong Lee, In‐Suk Choi, Changsoon Kim   +3 more
wiley   +1 more source

Optimizing Hyperparameters in Meta-Learning for Enhanced Image Classification

IEEE Access
This paper investigates the significance of hyperparameter optimization in meta-learning for image classification tasks. Despite advancements in deep learning, real-time image classification applications often suffer from data inadequacy.
Amala Mary Vincent, P. Jidesh, A. A. Bini   +2 more
doaj   +1 more source

Metamodel-Based Hyperparameter Optimization of Optimization Algorithms in Building Energy Optimization

Buildings, 2023
Building energy optimization (BEO) is a promising technique to achieve energy efficient designs. The efficacy of optimization algorithms is imperative for the BEO technique and is significantly dependent on the algorithm hyperparameters.
Binghui Si, Feng Liu, Yanxia Li
doaj   +1 more source

Predicting Atomic Charges in MOFs by Topological Charge Equilibration

Advanced Functional Materials, EarlyView.
An atomic charge prediction method is presented that is able to accurately reproduce ab‐initio‐derived reference charges for a large number of metal–organic frameworks. Based on a topological charge equilibration scheme, static charges that fulfill overall neutrality are quickly generated.
Babak Farhadi Jahromi, Sumukh Shankar Sharadaprasad, Rochus Schmid   +2 more
wiley   +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., Hutter, Frank, Leyton-Brown, Kevin, Thornton, Chris   +3 more
core   +2 more sources

Automatic Termination for Hyperparameter Optimization

, 2021
Bayesian optimization (BO) is a widely popular approach for the hyperparameter optimization (HPO) in machine learning. At its core, BO iteratively evaluates promising configurations until a user-defined budget, such as wall-clock time or number of iterations, is exhausted. While the final performance after tuning heavily depends on the provided budget,
Makarova, Anastasia, Shen, Huibin, Perrone, Valerio, Klein, Aaron, Faddoul, Jean Baptiste, Krause, Andreas, Seeger, Matthias, Archambeau, Cedric   +7 more
openaire   +3 more sources

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, Yunsik Ohm, Ren‐Mian Chin, Olivia Kerr, Krithika Manohar, Mohammad H. Malakooti   +5 more
wiley   +1 more source

Metaheuristics in automated machine learning: Strategies for optimization

Intelligent Systems with Applications
The present work explores the application of Automated Machine Learning techniques, particularly on the optimization of Artificial Neural Networks through hyperparameter tuning.
Francesco Zito, El-Ghazali Talbi, Claudia Cavallaro, Vincenzo Cutello, Mario Pavone   +4 more
doaj   +1 more source