ExpertMatcher: Automating ML Model Selection for Users in Resource Constrained Countries
By Vivek Sharma, Praneeth Vepakomma, Tristan Swedish, Kenglun Chang, Jayashree Kalpathy-Cramer, Ramesh Raskar. 2019
7 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence
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In Artificial Intelligence
- Praneeth Vepakomma
- Ramesh Raskar
- Abhishek Singh
- Vivek Sharma
- Tristan Swedish
- Jayashree Kalpathy-Cramer
- Kenglun Chang
- Alberto Roman
Citations in the corpus
- SplitNN-driven Vertical Partitioning (2020, methodology): “Vertical federated learning Learning with vertically partitioned data in the context of federated learning, a popular distributed deep learning paradigm was studied in (Nock et al. , 2018; Liu et al. , 2019, 2020; Sameer et al. , 2018; Vepakomma et al. , 2018b; Sharma et al. , 2019a,b).”
- ExpertMatcher: Automating ML Model Selection for Clients using Hidden Representations (2019, methodology): “The Expert matching problem as described by Sharma et al. (2019) is to assign input data from a client to the most appropriate expert model without any information other than the input data itself.”