# ExpertMatcher: Automating ML Model Selection for Users in Resource Constrained Countries

arXiv: https://arxiv.org/abs/1910.02312 · HTML: https://mlsanity.com/paper/1910.02312/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Oct 2019.

## Built on by (most cited first, 2 of 2)

- [AdaSplit: Adaptive Trade-offs for Resource-constrained Distributed Deep Learning](https://mlsanity.com/paper/2112.01637/) (2021)
- [Towards a Universal Gating Network for Mixtures of Experts](https://mlsanity.com/paper/2011.01613/) (2020)

## Abstract (arXiv)

In this work we introduce ExpertMatcher, a method for automating deep learning model selection using autoencoders. Specifically, we are interested in performing inference on data sources that are distributed across many clients using pretrained expert ML networks on a centralized server. The ExpertMatcher assigns the most relevant model(s) in the central server given the client's data representation. This allows resource-constrained clients in developing countries to utilize the most relevant ML models for their given task without having to evaluate the performance of each ML model. The method is generic and can be beneficial in any setup where there are local clients and numerous centralized expert ML models.