Vol. 3 No. 1 (2023): Issue 3
Articles

Device Performance Optimization through Variational Bayesian Inference

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  • Ji-soo Kim,
  • Min-hee Park,
  • Sang-won Lee

Published 2023-10-21

Keywords

  • Device Performance,
  • Optimizing,
  • Variational Bayesian Inference,
  • Probabilistic Modeling,
  • Technological Applications

How to Cite

Kim, J.- soo, Park, M.- hee, & Lee, S.- won. (2023). Device Performance Optimization through Variational Bayesian Inference. Optimizations in Applied Machine Learning, 3(1). Retrieved from https://ojs.mri-pub.com/index.php/OAML/article/view/35
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Abstract

Optimizing device performance is crucial in various technological applications. The current state of research in this field faces challenges in accurately modeling complex systems and efficiently identifying optimal operational parameters. In response to these challenges, this paper proposes a novel approach utilizing Variational Bayesian Inference to optimize device performance. By integrating probabilistic modeling with Bayesian inference techniques, our method enables more precise and efficient optimization of device parameters. Through extensive experimentation and analysis, we demonstrate the effectiveness of our approach in improving device performance across a range of applications. This research not only enhances our understanding of device optimization but also offers a practical and innovative solution for advancing technological capabilities.

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