Summary of Bayesian Optimized Deep Ensemble For Uncertainty Quantification Of Deep Neural Networks: a System Safety Case Study on Sodium Fast Reactor Thermal Stratification Modeling, by Zaid Abulawi et al.
Bayesian optimized deep ensemble for uncertainty quantification of deep neural networks: a system safety case study on sodium fast reactor thermal stratification modeling
by Zaid Abulawi, Rui Hu, Prasanna Balaprakash, Yang Liu
First submitted to arxiv on: 11 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed BODE method combines Bayesian optimization with deep ensembles to improve both predictive accuracy and uncertainty quantification in Deep Neural Networks. This approach enhances the performance of deep ensembles, which are efficient and scalable methods for uncertainty quantification, but are limited by simply retraining the same network multiple times with random initializations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In risk-sensitive fields like system safety modeling, accurate predictions and uncertainty quantification are crucial for decision-making. The BODE method is designed to overcome the limitations of traditional deep ensembles and provide better results. This approach combines the strengths of Bayesian optimization and deep ensembles to achieve more accurate predictions and better uncertainty quantification. |
Keywords
» Artificial intelligence » Optimization
