Discovery Early Career Researcher Award - Grant ID: DE240100144

Funding Activity

Website
http://dataportal.arc.gov.au/NCGP/Web/Grant/Grant/DE240100144

Funding Status
Active

Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the .

Funded Activity Summary

Universal Model Selection Criteria for Scientific Machine Learning. This project aims to develop provably reliable universal model selection criteria to facilitate trustworthy scientific machine learning. Combining stochastic methods with an innovative geometric approach to basic statistical principles, this project expects to characterise, combine, and refine the most successful heuristics for designing and training huge models, such as deep neural networks, into a cohesive theoretical framework. The expected outcomes include a general toolkit for assisting neural network design at the forefront of scientific applications. This should significantly improve the quality of scientific predictions by facilitating confident adoption of deep learning methods into the pantheon of trustworthy modeling techniques.

Funded Activity Details

Start Date: 01-01-2024

End Date: 31-12-2026

Funding Scheme: Discovery Early Career Researcher Award

Funding Amount: $444,447.00

Funder: Australian Research Council

Research Topics

ANZSRC Field of Research (FoR)

Deep learning | Machine learning | Statistical theory

ANZSRC Socio-Economic Objective (SEO)

Other Keywords

Application Software Packages | Deep learning | Expanding Knowledge In the Information and Computing Sciences | Expanding Knowledge In the Mathematical Sciences | Machine learning | Statistical theory

Related Links