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Grant Award View - GA120001-V3
Declarative Networks: Towards Robust and Explainable Deep Learning
GA ID:
GA120001-V3
Agency:
Australian Research Council
Approval Date:
26-Aug-2020
Variation Publish Date:
20-Oct-2021
Variation Date:
12-Oct-2021
Category:
Science, Technology, Engineering and Mathematics (STEM) Research
Grant Term:
1-Apr-2021 to 31-Mar-2025
Value (AUD):
$1,048,712.00
(GST inclusive where applicable)
Varies:
GA120001
- Declarative Networks: Towards Robust and Explainable Deep Learning
One-off/Ad hoc:
No
Aggregate Grant Award:
No
PBS Program Name:
ARC 20/21 Discovery
Grant Program:
ARC Future Fellowships
Grant Activity:
Declarative Networks: Towards Robust and Explainable Deep Learning
Purpose:
The aim of this project is to develop declarative machine learning techniques that exploit inherent structure and models of the world. Deep learning has become the dominant approach for machine learning with many products and promises built on this technology. But deep learning is expensive, opaque, brittle and relies solely on human labelled data. This project intends to make deep learning more reliable by establishing theory and algorithms that allow physical and mathematical models to be embedded within a deep learning framework, providing performance guarantees and interpretability. This would likely benefit machine learning based products that can understand the world and interact with humans naturally through vision and language.
GO ID:
GO Title:
Future Fellowships for funding commencing in 2020
Internal Reference ID:
FT20 Round 1
Selection Process:
Targeted or Restricted Competitive
Confidentiality - Contract:
No
Confidentiality - Outputs:
No
Grant Recipient Details
Recipient Name:
The Australian National University
Recipient ABN:
52 234 063 906
Grant Recipient Location
Suburb:
ACTON
Town/City:
ACTON
Postcode:
2601
State/Territory:
ACT
Country:
AUSTRALIA
Grant Delivery Location
State/Territory:
ACT
Postcode:
2601
Country:
AUSTRALIA