Search Results for author:
Found 6 papers, 0 papers with code
Optimizing Deep Learning Inference on Embedded Systems Through Adaptive Model Selection
For image classification, we achieve a 1. 8x reduction in inference time with a 7. 52% improvement in accuracy, over the most-capable single DNN model.
Transferable Knowledge for Low-cost Decision Making in Cloud Environments
We overcome this through developing a Transfer Learning (TL) approach where the knowledge (in the form of the prediction model and associated data set) gained from running an application on a particular cloud infrastructure is transferred in order to substantially reduce the overhead of building new models for the performance of new applications and/or cloud infrastructures.
Navigating Diverse Data Science Learning: Critical Reflections Towards Future Practice
Data Science is currently a popular field of science attracting expertise from very diverse backgrounds.
Adaptive Selection of Deep Learning Models on Embedded Systems
This paper presents an adaptive scheme to determine which DNN model to use for a given input, by considering the desired accuracy and inference time.
Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges
We provide a comprehensive survey highlighting the recent advancements in unsupervised learning techniques and describe their applications for various learning tasks in the context of networking.
Daleel: Simplifying Cloud Instance Selection Using Machine Learning
Decision making in cloud environments is quite challenging due to the diversity in service offerings and pricing models, especially considering that the cloud market is an incredibly fast moving one.
