# Structured Bayesian Compression for Deep models in mobile enabled devices for connected healthcare

## TL;DR

This paper proposes a structured Bayesian compression technique to reduce the size and computational demands of deep neural networks, enabling efficient deployment on mobile healthcare devices.

## Contribution

It introduces a novel Bayesian compression method tailored for deep models, optimizing them for mobile and connected healthcare applications.

## Key findings

- Significant reduction in model size and energy consumption
- Maintained high accuracy in medical data analysis
- Enhanced suitability for mobile healthcare devices

## Abstract

Deep Models, typically Deep neural networks, have millions of parameters, analyze medical data accurately, yet in a time-consuming method. However, energy cost effectiveness and computational efficiency are important for prerequisites developing and deploying mobile-enabled devices, the mainstream trend in connected healthcare.

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Source: https://tomesphere.com/paper/1902.05429