Results 71 to 80 of about 2,251,103 (302)

On Batch Bayesian Optimization

CoRR, 2019
All of Bayesian Nonparametrics workshop, Neural Information Processing Systems ...
Sayak Ray Chowdhury, Aditya Gopalan
openaire   +3 more sources

dynoGP: Deep Gaussian Processes for Dynamic System Identification

International Journal of Adaptive Control and Signal Processing, EarlyView.
This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli, Marco Forgione, Dario Piga, Marco Zaffalon   +3 more
wiley   +1 more source

The impact of Bayesian optimization on feature selection

Scientific Reports
Feature selection is an indispensable step for the analysis of high-dimensional molecular data. Despite its importance, consensus is lacking on how to choose the most appropriate feature selection methods, especially when the performance of the feature ...
Kaixin Yang, Long Liu, Yalu Wen
doaj   +1 more source

Application of Bayesian optimized XGBoost in seismic interpretation of small-scale faults

Meitan xuebao, 2023
In order to further improve the identification accuracy of small-scale faults in seismic interpretation, Bayesian optimized extreme gradient boosting (XGBoost) model was constructed to recognize small-scale faults across coalbeds using reduced seismic ...
Changwei DING, Xin WANG, Tongjun CHEN, Ting YU   +3 more
doaj   +1 more source

Bayesian Optimization Based on K-Optimality [PDF]

Entropy, 2018
Bayesian optimization (BO) based on the Gaussian process (GP) surrogate model has attracted extensive attention in the field of optimization and design of experiments (DoE). It usually faces two problems: the unstable GP prediction due to the ill-conditioned Gram matrix of the kernel and the difficulty of determining the trade-off parameter between ...
Liang Yan 0003, Xiaojun Duan, Bowen Liu, Jin Xu   +3 more
openaire   +4 more sources

Bayesian Optimization with Expensive Integrands

SIAM Journal on Optimization, 2022
We propose a Bayesian optimization algorithm for objective functions that are sums or integrals of expensive-to-evaluate functions, allowing noisy evaluations. These objective functions arise in multi-task Bayesian optimization for tuning machine learning hyperparameters, optimization via simulation, and sequential design of experiments with random ...
Saul Toscano-Palmerin, Peter I. Frazier
openaire   +4 more sources

Characterization of Defect Distribution in an Additively Manufactured AlSi10Mg as a Function of Processing Parameters and Correlations with Extreme Value Statistics

Advanced Engineering Materials, EarlyView.
Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt, Varma Kvvssn, Hossein Laieghi, Zia Uddin, Peyman Ansari, E. Kıvanç Sadak, Can Barış Toprak, Hüseyin Kızıl, Enrico Salvati   +8 more
wiley   +1 more source

BAYESIAN OPTIMIZATION FOR TUNING HYPERPARAMETRS OF MACHINE LEARNING MODELS: A PERFORMANCE ANALYSIS IN XGBOOST

Компютерні системи та інформаційні технології
The performance of machine learning models depends on the selection and tuning of hyperparameters. As a widely used gradient boosting method, XGBoost relies on optimal hyperparameter configurations to balance model complexity, prevent overfitting, and ...
Микола ЗЛОБІН, Володимир БАЗИЛЕВИЧ   +1 more
doaj   +1 more source

Bayesian Solution Uncertainty Quantification for Differential Equations [PDF]

, 2016
We explore probability modelling of discretization uncertainty for system states defined implicitly by ordinary or partial differential equations. Accounting for this uncertainty can avoid posterior under-coverage when likelihoods are constructed from a ...
Girolami, MA, Campbell, DA, Calderhead, B, ,, Chkrebtii, OA   +4 more
core   +1 more source

A Workflow to Accelerate Microstructure‐Sensitive Fatigue Life Predictions

Advanced Engineering Materials, EarlyView.
This study introduces a workflow to accelerate predictions of microstructure‐sensitive fatigue life. Results from frameworks with varying levels of simplification are benchmarked against published reference results. The analysis reveals a trade‐off between accuracy and model complexity, offering researchers a practical guide for selecting the optimal ...
Luca Loiodice, Krzysztof S. Stopka, Michael D. Sangid   +2 more
wiley   +1 more source