Article

Scale-Up Implementation of a Transportation Network Using Ant Colony Optimization (ACO)

In this article an OpenMP* based implementation of the Ant Colony Optimization algorithm was analyzed for bottlenecks with Intel® VTune™ Amplifier XE 2016 together with improvements using hybrid MPI-OpenMP and Intel® Threading Building Blocks were introduced to achieve efficient scaling across a four-socket Intel® Xeon® processor E7-8890 v4 processor-based system.
Criado por Sunny G. (Intel) Última atualização em 05/07/2019 - 19:10
Mensagem de blog

SHEPHERD: Just when I thought I was out... they pull me back in

Don't get me wrong, I was a quite willing participant with all of this. 

Criado por Última atualização em 10/07/2018 - 08:08
Article

Case Study – Using the Intel® Deep Learning SDK for Training Image Recognition Models

In this case study, we explore LeNet*,one of the prominent image recognition topologies for handwritten digit recognition, and show how the training tool can be used to visually set up, tune, and train the Mixed National Institute of Standards and Technology (MNIST) dataset on Caffe* optimized for Intel® architecture. Data scientists are the intended audience.
Criado por Meghana R. (Intel) Última atualização em 24/01/2018 - 15:35
Article

Unattended Baggage Detection Using Deep Neural Networks in Intel® Architecture

In a world becoming ever more attuned to potential security threats, the need to deploy sophisticated surveillance systems is increasing. This article discusses inferencing a Microsoft Common Objects in Context (MS-COCO) detection model for detecting unattended baggage in a train station.
Criado por administrar Última atualização em 08/05/2018 - 09:38
Article

Power System Infrastructure Monitoring Using Deep Learning on Intel® Architecture

The work in this paper evaluates the performance of Intel® Xeon® processor powered machines for running deep learning on the GoogleNet* topology (Inception* v.
Criado por administrar Última atualização em 08/05/2018 - 09:20
Mensagem de blog

Track Reconstruction with Deep Learning at the CERN CMS Experiment

This blog post is part of a series that describes my summer school project at CERN openlab.

Criado por Última atualização em 21/03/2019 - 12:00
Article

Faster Convolutional Neural Network Models Improve the Screening of Cervical Cancer

The goal of the Kaggle* Competition sponsored by Intel and MobileODT* was to use artificial intelligence to improve the precision and accuracy of cervical cancer screening. This case study follows the process used by the first-place-winning team, TEST (Towards Empirically Stable Training), to create an algorithm that would improve this life-saving diagnostic procedure.
Criado por administrar Última atualização em 15/02/2019 - 14:39
Article

Deep Learning Improves Cervical Cancer Accuracy by 81%, Using Intel® Technology

The goal of the Kaggle* Competition sponsored by Intel and MobileODT* was to use artificial intelligence to improve the precision and accuracy of cervical cancer screening. This case study details the process used by special prize winner Luis Andre Dutra e Silva to improve cervical cancer screenings.
Criado por administrar Última atualização em 18/02/2019 - 14:26
Article

How Can AI Advance Cervical Cancer Detection Using Convolutional Neural Networks

The goal of the Kaggle* Competition sponsored by Intel and MobileODT* was to use artificial intelligence to improve the precision and accuracy of cervical cancer screening. This case study follows the process used by second-place winner Indrayana Rustandi to build a deep learning model improving this life-saving diagnostic procedure.
Criado por administrar Última atualização em 15/08/2019 - 12:51
Article

Testing of Six Different AI-Based Models: A Deep Dive to Improve Cervical Cancer Screening

The goal of the Kaggle* Competition sponsored by Intel and MobileODT* was to use artificial intelligence to improve the precision and accuracy of cervical cancer screening. This case study follows the process used by the third-place winning team, GRXJ. They pooled their respective skill sets to create an algorithm that would improve this life-saving diagnostic procedure.
Criado por administrar Última atualização em 21/02/2019 - 11:16