samuelkim314/DeepBO
Deep Bayesian Optimization for Problems with High-Dimensional Structure
This project helps scientists and engineers efficiently optimize complex scientific experiments, especially when working with high-dimensional data like images or graphs. It takes in experimental data, potentially with auxiliary information, and uses advanced algorithms to suggest better experiment parameters, helping you find optimal designs or settings more quickly than traditional methods. It's designed for researchers who need to explore many parameters for problems like designing new materials or chemical compounds.
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Use this if you need to optimize expensive, 'black-box' scientific or engineering processes with many input parameters, and you want to leverage deep learning to speed up your search for optimal solutions.
Not ideal if your optimization problem has a small number of parameters, or if your data doesn't benefit from deep learning models.
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Language
Python
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Last pushed
Sep 26, 2022
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