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Machine Learning on the Edge

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An efficient open-source implementation that trains ProtoNN and outputs small machine learning models capable of being deployed on resource-constrained devices.

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ProtoNN: Compressed and Accurate kNN for Resource-Scarce Devices

Published in International Conference on Machine Learning, 2017

Machine learning for resource-constrained scenarios such as IoT.

Recommended citation: Gupta, Chirag, Arun Sai Suggala, Ankit Goyal, Harsha Vardhan Simhadri, Bhargavi Paranjape, Ashish Kumar, Saurabh Goyal, Raghavendra Udupa, Manik Varma, and Prateek Jain. "ProtoNN: Compressed and Accurate kNN for Resource-scarce Devices." In International Conference on Machine Learning, pp. 1331-1340. 2017. http://proceedings.mlr.press/v70/gupta17a.html

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