Search Results for author:
Found 6 papers, 4 papers with code
Ammunition Component Classification Using Deep Learning
The trained models are tested against unseen data in order to evaluate the performance of the applied method.
Real-Time Accident Detection in Traffic Surveillance Using Deep Learning
This paper presents a new efficient framework for accident detection at intersections for traffic surveillance applications.
Persia: An Open, Hybrid System Scaling Deep Learning-based Recommenders up to 100 Trillion Parameters
Specifically, in order to ensure both the training efficiency and the training accuracy, we design a novel hybrid training algorithm, where the embedding layer and the dense neural network are handled by different synchronization mechanisms; then we build a system called Persia (short for parallel recommendation training system with hybrid acceleration) to support this hybrid training algorithm.
Smart Traffic Monitoring System using Computer Vision and Edge Computing
In this paper, we focus on two common traffic monitoring tasks, congestion detection, and speed detection, and propose a two-tier edge computing based model that takes into account of both the limited computing capability in cloudlets and the unstable network condition to the TMC.
BAGUA: Scaling up Distributed Learning with System Relaxations
Recent years have witnessed a growing list of systems for distributed data-parallel training.
A Novel Locally Linear KNN Model for Visual Recognition
And then the locally linear KNN model based classifier (LLKNNC), which shows its connection to the Bayes decision rule for minimum error in the view of kernel density estimation, is proposed for classification.
