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 ...

Deep learning has enjoyed much recent success, and applying state-of-the-art model learning methods to controls is an exciting prospect. However, there is a strong reluctance to use these methods on ...

The high penetration of Renewable Energy Sources (RES) into the grid introduces complexity to the operation and optimization of energy. One potential solution to the challenge is to use deep ...

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 ...

Therefore, to close these gaps, this research proposes a novel deep learning-based Bayesian calibration framework, involving pre-calibration mechanism, Long Short-Term Memory as surrogate models, and ...

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 ...