Bayesian optimization is a powerful technique for finding the optimal values of hyperparameters in machine learning models. Hyperparameters are the settings that control how the model learns from ...

This paper introduces an intelligent optimization framework that integrates Digital Twin (DT) technology, deep learning, and a tailored Multi-Restart Bayesian Optimization with Random Initialization ...

In conclusion, the Embed-then-Regress method showcases the flexibility of string-based in-context regression for Bayesian Optimization across diverse problems, achieving results comparable to standard ...

But it is important to note that Bayesian optimization does not itself involve machine learning based on neural networks, but what IBM is in fact doing is using Bayesian optimization and machine ...

The fully Bayesian treatment of the latter allows additional control over the optimization via the selection of priors for the model parameters. The method is demonstrated for a noisy version of a ...

Scanning tunneling microscopy (STM) is a widely used tool for atomic imaging of novel materials and their surface energetics. However, the optimization of the imaging conditions is a tedious process ...