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

02/13/2019
by   Sijia Chen, et al.
0

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.

READ FULL TEXT
research
09/11/2020

A Review on Security and Privacy of Internet of Medical Things

The Internet of Medical Things (IoMT) are increasing the accuracy, relia...
research
09/29/2015

Compression of Deep Neural Networks on the Fly

Thanks to their state-of-the-art performance, deep neural networks are i...
research
02/01/2019

Towards Collaborative Intelligence Friendly Architectures for Deep Learning

Modern mobile devices are equipped with high-performance hardware resour...
research
04/11/2022

Autonomous Mobile Clinics: Empowering Affordable Anywhere Anytime Healthcare Access

We are facing a global healthcare crisis today as the healthcare cost is...
research
11/28/2019

Data-Driven Compression of Convolutional Neural Networks

Deploying trained convolutional neural networks (CNNs) to mobile devices...
research
09/28/2021

Which Design Decisions in AI-enabled Mobile Applications Contribute to Greener AI?

Background: The construction, evolution and usage of complex artificial ...
research
02/23/2016

Mobile Big Data Analytics Using Deep Learning and Apache Spark

The proliferation of mobile devices, such as smartphones and Internet of...

Please sign up or login with your details

Forgot password? Click here to reset