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Found 6 papers, 3 papers with code
Decreasing the Computing Time of Bayesian Optimization using Generalizable Memory Pruning
These long computing times are a result of the Gaussian process surrogate model having a polynomial time complexity with the number of experiments.
Using Scalable Computer Vision to Automate High-throughput Semiconductor Characterization
High-throughput materials synthesis methods have risen in popularity due to their potential to accelerate the design and discovery of novel functional materials, such as solution-processed semiconductors.
Human Evaluation of Text-to-Image Models on a Multi-Task Benchmark
For example, asking a model to generate a varying number of the same object to measure its ability to count or providing a text prompt with several objects that each have a different attribute to identify its ability to match objects and attributes correctly.
Fast Bayesian Optimization of Needle-in-a-Haystack Problems using Zooming Memory-Based Initialization (ZoMBI)
Needle-in-a-Haystack problems exist across a wide range of applications including rare disease prediction, ecological resource management, fraud detection, and material property optimization.
A Machine Learning and Computer Vision Approach to Rapidly Optimize Multiscale Droplet Generation
Generating droplets from a continuous stream of fluid requires precise tuning of a device to find optimized control parameter conditions.
Online Preconditioning of Experimental Inkjet Hardware by Bayesian Optimization in Loop
Thus, in this work, we develop a computer vision-driven Bayesian optimization framework for optimizing the deposited droplet structures from an inkjet printer such that it is tuned to perform high-throughput experimentation on semiconductor materials.
