Results 141 to 150 of about 42,332 (292)
Practical Bayesian optimisation for hyperparameter tuning.
, 2020 Advances in machine learning have had, and continue to have, a profound effect on scientific research and industrial activities. We are able to uncover insights contained within large troves of data and develop models to solve problems that seemed infeasible until recently.
openaire +3 more sources
Discriminator‐Guided Inverse Folding for Multi‐Property Protein Design
Advanced Science, EarlyView.Discriminator‐Guided Inverse Folding (DGIF) integrates multiple property predictors trained from single‐property datasets to guide protein sequence generation from a backbone structure. DGIF enables simultaneous improvement of thermostability and solubility without requiring multi‐property annotated datasets and generates designs that move toward the ...
Yuchuan Zheng +7 more
wiley +1 more source
The Use of Hyperparameter Tuning in Model Classification: A Scientific Work Area Identification
JOIV: International Journal on Informatics VisualizationThis research aims to investigate the effectiveness of hyperparameter tuning, particularly using Optuna, in enhancing the classification performance of machine learning models on scientific work reviews. The study focuses on automating the classification
Nadya Alinda Rahmi +2 more
doaj +1 more source
Advanced Science, EarlyView.A machine learning‐assisted framework optimizes the KCl‐CaCl2‐LiCl ternary electrolyte. The optimized 13:35:52 mol% composition enables Ca‐based liquid metal batteries to operate stably at 480 °C, with >99.5% coulombic efficiency, ultralow self‐discharge, and excellent cycling stability, advancing low‐temperature large‐scale energy storage.
Xinglin Zhou +3 more
wiley +1 more source
wwu_tinker : A free hyperparameter tuning tool
, 2018 While machine learning model parameters can be learned from a set of training data, training machine learning models almost always requires setting hyperparameters that cannot be learned from the data (e.g.
Mooneyham, Jonny, New, Zach
core
Hyperparameter Optimization of Ensemble Learning for Heart Disease Prediction using Patient Data
Sistemasi: Jurnal Sistem InformasiThis study evaluates the impact of hyperparameter optimization on the performance of four machine learning algorithms—Extra Trees, XGBoost, Random Forest, and AdaBoost—in heart disease prediction. The results show that hyperparameter tuning significantly
Nikko Listio Wicaksono, Kusrini Kusrini
doaj +1 more source
Advanced Science, EarlyView.Blood‐based amino acid patterns measured by 19F NMR reveal hidden metabolic changes in colorectal cancer. By analyzing how these amino acids interact as a network, machine learning models identify patients at higher risk of recurrence and metastasis.
Ji‐Yeon Lee +9 more
wiley +1 more source
Practical Differentially Private Hyperparameter Tuning with Subsampling
Tuning the hyperparameters of differentially private (DP) machine learning (ML) algorithms often requires use of sensitive data and this may leak private information via hyperparameter values.
Kulkarni, Tejas, Koskela, Antti
core
Uncertainty‐Aware Deep Ensembles for Robust and Reliable Chemical Sensor Arrays
Advanced Science, EarlyView.A reliability‐aware electronic nose is developed using photothermally anchored metal‐catalyst decorated metal oxide nanofiber sensor arrays combined with deep ensemble learning. Diverse catalytic nanofiber channels generate gas‐specific response patterns, enabling selective identification and quantification of sulfur‐containing gases.
Sungwoo Eo +5 more
wiley +1 more source
Advanced Science, EarlyView.A versatile framework integrates addressable electrothermal actuation and strain‐constraint mechanisms to construct programmable shape‐morphing soft matter systems. By combining an analytical inverse design strategy for high‐fidelity 3D surface reconstruction with deep learning‐based closed‐loop control, this approach enables zero‐energy shape locking,
Kai Liu +5 more
wiley +1 more source