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英文字典中文字典相关资料:


  • How Diverse Initial Samples Help and Hurt Bayesian Optimizers
    Specifically, we test how providing diverse versus non-diverse initial samples to BO affects its performance during search and introduce a fast ranked-determinantal point process method for computing diverse sets, which we need to detect sets of highly diverse or non-diverse initial samples
  • How Diverse Initial Samples Help and Hurt Bayesian Optimizers
    Researchers have struggled to 36 produce quantitative predictions or explanations for exactly 37 why and when diversity might help or hinder design search 38 efforts
  • IDEALLab JMD-Diversity-in-Bayesian-Optimization - GitHub
    Indeed, we show that fixing the BO hyper-parameters removes the Model Building advantage, causing diverse initial samples to always outperform models trained with non-diverse samples These findings shed light on why, at least for BO-type optimizers, the use of diversity has mixed effects and cautions against the ubiquitous use of space-filling
  • EFFECTS OF DIVERSE INITIALIZATION ON BAYESIAN OPTIMIZERS
    Specifically, we test how providing diverse versus non-diverse initial samples to BO affects its performance during search and introduce a fast ranked-DPP method for computing diverse sets, which we need to detect sets of highly diverse or non-diverse initial samples
  • Structured sampling strategies in Bayesian optimization . . .
    Poor sampling can lead to uneven coverage, overlooking crucial regions, and weakening the initial surrogate model; which can significantly hinder overall BO performance Despite this importance, prior studies have largely focused on BO’s overall efficiency and success without systematically investigating the impact of diverse structured
  • How Diverse Initial Samples Help and Hurt Bayesian Optimizers
    Specifically, we test how providing diverse versus non-diverse initial samples to BO affects its performance during search and introduce a fast ranked-determinantal point process method for computing diverse sets, which we need to detect sets of highly diverse or non-diverse initial samples
  • Joel Chan | Papers
    We show that fixing the BO hyper-parameters removes the Model Building advantage, causing diverse initial samples to always outperform models trained with non-diverse samples These findings shed light on why, at least for BO-type optimizers, the use of diversity has mixed effects and cautions against the ubiquitous use of space-filling





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