64 citations · 85 across the 22 of their papers we have counts for
3 papers · 1 filter
Exploring the Boundaries of On-Device Inference: When Tiny Falls Short, Go Hierarchical
Adarsh Prasad Behera, Paulius Daubaris, Iñaki Bravo +4
On-device inference holds great potential for increased energy efficiency, responsiveness, and privacy in edge ML systems. However, due to less capable ML models that can be embedd…
Error Bounds for the Network Scale-Up Method
Sergio Díaz-Aranda, Juan Marcos Ramírez, Mohit Daga +4
Epidemiologists and social scientists have used the Network Scale-Up Method (NSUM) for over thirty years to estimate the size of a hidden sub-population within a social network. Th…
Improved Decision Module Selection for Hierarchical Inference in Resource-Constrained Edge Devices
Adarsh Prasad Behera, Roberto Morabito, Joerg Widmer +1
The Hierarchical Inference (HI) paradigm employs a tiered processing: the inference from simple data samples are accepted at the end device, while complex data samples are offloade…