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Machine learning for resource-constrained devices.
An efficient open-source implementation that trains ProtoNN and outputs small machine learning models capable of being deployed on resource-constrained devices.
Faster algorithms for optimization problems with L0 (sparsity) constraints.
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