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Every child should have the opportunity to achieve all they are capable of. Through no fault of their own, children experiencing poverty often start school behind their peers. Without access to the vital resources, opportunities, and support that all children need to achieve their best, they...
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Machine learning has recently made significant progress for medical imaging applications including image segmentation, enhancement, and reconstruction. Funded as an Australian Research Council
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similar fragmentation patterns. There is a critical need for generative frameworks that can learn from limited data by intelligently querying the most informative samples, and that can fuse multiple views
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the greatest need will receive the highest value scholarships. About the scholarship The Learning Potential Fund provides support through scholarships and bursaries to students in financial need.
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. Students with the greatest need will receive the highest value scholarships. About the scholarship The Learning Potential Fund provides support through scholarships and bursaries to students in financial
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based on need. Students with the greatest need will receive the highest value scholarships. About the scholarship The Learning Potential Fund provides support through scholarships and bursaries
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This project aims to identify novel methods for inferring actors, activities, and other elements from short message communications. Covert communications are a specialist domain for analysis in the Law Enforcement (LE) context. In this project we aim to improve law enforcement’s understanding of...
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Current federated learning architectures in mobile healthcare are limited to a centralised model without considering the full continuum of mobile-edge-cloud. Additionally, to support different data
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Background and Motivation Modern deep learning models have achieved remarkable success in computer vision and natural language processing. However, they typically produce overconfident predictions
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. This is indeed what AIC, BIC, MDL and MML would anticipate. And yet deep learning methods can often work despite this. This project investigates how deep learning can survive over-fitting and whether