OASI: Objective-Aware Surrogate Initialization for Multi-Objective Bayesian Optimization in TinyML Keyword Spotting
Analysis
This article introduces OASI, a method for improving multi-objective Bayesian optimization in TinyML, specifically for keyword spotting. The focus is on initializing surrogate models in a way that is aware of the objectives. The source is ArXiv, indicating a research paper.
Key Takeaways
Reference / Citation
View Original"OASI: Objective-Aware Surrogate Initialization for Multi-Objective Bayesian Optimization in TinyML Keyword Spotting"