Our study outlines the proof-of-principle that neuroimaging models for brain-age prediction can use Bayesian optimization to derive case-specific pre-processing parameters. Our results suggest that ...

The general workflow for Bayesian optimization in the context of neural network hyperparameter optimization and architecture search is as follows Environmental variables defining the search space and ...

Chen Liang, Sankaran Mahadevan, Multidisciplinary Optimization under Uncertainty Using Bayesian Network, SAE International Journal of Materials and Manufacturing, Vol. 9, No. 2 (May 2016), pp. 419-429 ...

Bayesian Optimization (BO) has become an essential framework for optimizing black-box, expensive, and high-dimensional functions, particularly in experimental workflows where exhaustive exploration is ...

High-intensity laser systems present unique measurement and optimization challenges due to their high complexity, low repetition rates, and shot-to-shot variations. We discuss recent developments ...

Nikolay Bliznyuk, David Ruppert, Christine Shoemaker, Rommel Regis, Stefan Wild, Pradeep Mugunthan, Bayesian Calibration and Uncertainty Analysis for Computationally Expensive Models Using ...

Bayesian optimization (BO) can help determine the optimal deposition conditions for high-performance passivation films in solar cells. However, simple implementations tend to suggest excessively ...