Summary of Composite Bayesian Optimization in Function Spaces Using Neon — Neural Epistemic Operator Networks, by Leonardo Ferreira Guilhoto et al.
Composite Bayesian Optimization In Function Spaces Using NEON – Neural Epistemic Operator Networks
by Leonardo Ferreira Guilhoto, Paris Perdikaris
First submitted to arxiv on: 3 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Information Theory (cs.IT); 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 Medium Difficulty summary: This paper introduces Neural Epistemic Operator Networks (NEON), a novel architecture for generating predictions with uncertainty using a single operator network backbone. Unlike deep ensembles, NEON requires orders of magnitude fewer trainable parameters to achieve comparable performance. The authors demonstrate the utility of this method in sequential decision-making scenarios, specifically composite Bayesian Optimization (BO). They compare NEON to state-of-the-art methods on toy and real-world scenarios, showing that NEON achieves state-of-the-art performance while reducing computational requirements. This work has implications for scientific computing and machine learning applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: Imagine a way to make predictions with some uncertainty built-in. That’s what Neural Epistemic Operator Networks (NEON) does. It’s an innovative approach that uses a single network to make predictions, unlike other methods that use many networks. The authors show how this works well for making decisions over time, like optimizing functions. They compare their method to others and find it performs just as well but with much less computation needed. |
Keywords
* Artificial intelligence * Machine learning * Optimization
