Summary of Explainable Data-driven Modeling Via Mixture Of Experts: Towards Effective Blending Of Grey and Black-box Models, by Jessica Leoni et al.
Explainable data-driven modeling via mixture of experts: towards effective blending of grey and black-box models
by Jessica Leoni, Valentina Breschi, Simone Formentin, Mara Tanelli
First submitted to arxiv on: 30 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY)
GrooveSquid.com Paper Summaries
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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 framework combines traditional first-principle-based models with machine learning approaches to achieve a balance between accuracy and complexity. A “mixture of experts” rationale enables the fusion of diverse local models, leveraging the strengths of both methods. The approach supports independent training of experts and allows for collaborative or competitive learning paradigms. To enhance interpretability, abrupt variations in expert combination are penalized. Experimental results demonstrate the effectiveness of the framework in producing an interpretable combination of models that closely resembles target phenomena. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper combines two different approaches to make a better model. The first approach is based on physical rules and the second is machine learning. They join these two together so they can be both accurate and easy to understand. It lets them train each part separately, then combine them in different ways. To make it even more understandable, they added a rule that prevents big changes in how the parts are combined. This works really well and makes the model look like what they were trying to predict. |
Keywords
* Artificial intelligence * Machine learning * Mixture of experts
