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Grant Award View - GA29213-V1
Feature-dependent label noise learning for big data analytics
GA ID:
GA29213-V1
Agency:
Australian Research Council
Approval Date:
27-Nov-2018
Variation Publish Date:
30-Jan-2019
Variation Date:
1-Jun-2019
Category:
Science, Technology, Engineering and Mathematics (STEM) Research
Grant Term:
1-Jun-2019 to 31-May-2022
Value (AUD):
$387,000.00
(GST inclusive where applicable)
Varies:
GA29213
- Feature-dependent label noise learning for big data analytics
One-off/Ad hoc:
No
Aggregate Grant Award:
No
PBS Program Name:
ARC 18/19 Discovery
Grant Program:
Discovery Early Career Researcher Award
Grant Activity:
Feature-dependent label noise learning for big data analytics
Purpose:
This project aims to equip machines with the ability to robustly harness feature-dependent label noise from big data. The project expects to produce the potential to explore and exploit the weakly supervised information to better understand, interpret, and infer big data. Expected outcomes of this project include theoretical foundations for learning with label noise in the real-world scenarios and the next generation of intelligent systems to accommodate noisily annotated big data. This project should benefit science, society, and the economy nationally and internationally through the applications in the areas of artificial intelligence, cybersecurity, and big data analytics.
GO ID:
GO Title:
Discovery Early Career Researcher Award commencing in 2019
Internal Reference ID:
DE19 Round 1
Selection Process:
Targeted or Restricted Competitive
Confidentiality - Contract:
No
Confidentiality - Outputs:
No
Grant Recipient Details
Recipient Name:
The University of Sydney
Recipient ABN:
15 211 513 464
Grant Recipient Location
Suburb:
FOREST LODGE
Town/City:
FOREST LODGE
Postcode:
2037
State/Territory:
NSW
Country:
AUSTRALIA
Grant Delivery Location
State/Territory:
NSW
Postcode:
2037
Country:
AUSTRALIA