Results 91 to 100 of about 1,872,984 (299)

No-Regret Bayesian Optimization with Unknown Hyperparameters

open access: yesJ. Mach. Learn. Res., 2019
ISSN:1532 ...
Felix Berkenkamp   +2 more
openaire   +5 more sources

Brain tumor classification from MRI scans: a framework of hybrid deep learning model with Bayesian optimization and quantum theory-based marine predator algorithm

open access: yesFrontiers in Oncology
Brain tumor classification is one of the most difficult tasks for clinical diagnosis and treatment in medical image analysis. Any errors that occur throughout the brain tumor diagnosis process may result in a shorter human life span.
Muhammad Sami Ullah   +5 more
doaj   +1 more source

Optimizing NiMo Electrocatalyst Coatings for Hydrogen Evolution in Alkaline Electrolysis With The Machine Learning‐Guided Autonomous Platform CatBot

open access: yesAdvanced Functional Materials, EarlyView.
This work presents a closed‐loop optimization workflow integrating CatBot, an automated synthesis and testing system, to discover NiMo hydrogen‐evolution catalysts. CatBot synthesised and evaluated 228 unique samples, identifying the best performing catalysts for transfer to nickel‐foam electrodes. The best candidate is highly active, reaching 1 Acm−2${
Paolo Vincenzo Freiesleben de Blasio   +7 more
wiley   +1 more source

Adaptive Optimizer for Automated Hyperparameter Optimization Problem

open access: yesCoRR, 2022
The choices of hyperparameters have critical effects on the performance of machine learning models. In this paper, we present a general framework that is able to construct an adaptive optimizer, which automatically adjust the appropriate algorithm and parameters in the process of optimization.
openaire   +2 more sources

Full‐Field Damage Monitoring in Architected Lattices Using In situ Electrical Impedance Tomography

open access: yesAdvanced Functional Materials, EarlyView.
In situ electrical impedance tomography (EIT) turns 3D‐printed, CNT‐infused architected lattices into full‐field damage‐imaging systems. Tunable Voronoi‐based geometries act as active sensing architectures, enabling conductivity maps to detect early‐stage damage and localise sequential ligament fracture before catastrophic failure.
Akash Deep   +4 more
wiley   +1 more source

Effect of hyperparameter tuning of machine learning algorithms on the modeling quality of the distribution of three mosquito species in Morocco

open access: yesJournal of Intelligent Systems
The widespread use of machine learning algorithms in dataset modeling requires a thorough understanding of the various tools likely to improve the modeling quality.
Douider Meriem   +2 more
doaj   +1 more source

Grayscale Photomasks Enable Noncontact Optical Patterning of Hydroxypropyl Cellulose Gels for Dynamic Art

open access: yesAdvanced Functional Materials, EarlyView.
Mask‐encoded UV exposure translates digital color information into pixel‐resolved structural‐color patterns in hydroxypropyl cellulose gels. Gaussian‐process‐guided calibration links UV processing conditions, wavelength, and mask opacity, enabling parallel fabrication of multicolor images in a single 10 s exposure.
Mengting Ye   +8 more
wiley   +1 more source

Hyperparameters tuning of random forest with harmony search in credit scoring [PDF]

open access: yes, 2019
Correct identification of defaulters and non-defaulters in the lending industry is a crucial task for financial institutions. Credit scoring is a tool utilized for credit granting decisions.
Goh, Rui Ying   +2 more
core   +1 more source

Dynamic Observation of Lacy Pattern Formation in a Bone Tissue Model Using Liquid Phase Scanning Transmission Electron Microscopy

open access: yesAdvanced Functional Materials, EarlyView.
Here, we establish a low‐dose liquid‐phase electron microscopy workflow for real‐time observation of biological processes in hydrated conditions. Beam damage is reduced by imaging only 20% of the pixels and reconstructing the complete image using an inpainting algorithm.
Luco Rutten   +7 more
wiley   +1 more source

Hyperparameter Optimization in Machine Learning

open access: yesFoundations and Trends® in Machine Learning
Hyperparameters are configuration variables controlling the behavior of machine learning algorithms. They are ubiquitous in machine learning and artificial intelligence and the choice of their values determines the effectiveness of systems based on these technologies.
Franceschi, Luca   +7 more
openaire   +4 more sources

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