A Novel Automatic Neural Network Feature Extractor. This project aims to study feature extraction abilities of convolutional as well as traditional neural networks and develop a generic feature extractor which can be applied to wide variety of real-world image and non-image data. New concepts for automatic feature extraction, feature explanation, hybrid evolutionary algorithms and non-iterative ensemble learning will be introduced and evaluated. The expected outcomes are a generic feature extrac ....A Novel Automatic Neural Network Feature Extractor. This project aims to study feature extraction abilities of convolutional as well as traditional neural networks and develop a generic feature extractor which can be applied to wide variety of real-world image and non-image data. New concepts for automatic feature extraction, feature explanation, hybrid evolutionary algorithms and non-iterative ensemble learning will be introduced and evaluated. The expected outcomes are a generic feature extractor for automatically extracting features, an optimiser for finding optimal parameters and non-iterative ensemble learning technique for classification of features into classes. The impact of this project will be automatic feature extractors and classifiers for real-world applications.Read moreRead less
Quantum-Inspired Machine Learning. This project aims to develop new machine learning techniques based around the close correspondence between
neural networks used in deep learning, and tensor networks used in quantum physics. Tensor networks are a form of information compression that is useful in machine learning to construct a compact representation of a large data set in a way that is more amenable to understanding the internal structure than a deep neural network. Expected outcomes of this pr ....Quantum-Inspired Machine Learning. This project aims to develop new machine learning techniques based around the close correspondence between
neural networks used in deep learning, and tensor networks used in quantum physics. Tensor networks are a form of information compression that is useful in machine learning to construct a compact representation of a large data set in a way that is more amenable to understanding the internal structure than a deep neural network. Expected outcomes of this project include more resilient algorithms for machine learning, and new ways to represent quantum states that will impact fundamental physics. The resulting benefits include enhanced capacity for cross-discipline collaboration, and improved methods for future industrial applications.
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Discovery Early Career Researcher Award - Grant ID: DE120102948
Funder
Australian Research Council
Funding Amount
$375,000.00
Summary
Interactive computer vision for image interpretation. This project aims at pushing forward the fundamental research in interactive computer vision. The outcome of this project will enable reliable and efficient solutions to real world image interpretation tasks, such as medical image analysis, financial document processing, and impact evaluation from natural disasters.
Special Research Initiatives - Grant ID: SR0354696
Funder
Australian Research Council
Funding Amount
$30,000.00
Summary
ARC Research Network in Enterprise Information Infrastructure (EII). This research network targets investigation of Enterprise Computing and its infrastructure, with an emphasis on emerging advanced technologies and practices, for large-scale enterprises, government agencies and community groups. EII will bring together the best IT researchers, leading edge users and key IT technology providers to support consolidated, technically sound, integrated and strategically positioned research towards s ....ARC Research Network in Enterprise Information Infrastructure (EII). This research network targets investigation of Enterprise Computing and its infrastructure, with an emphasis on emerging advanced technologies and practices, for large-scale enterprises, government agencies and community groups. EII will bring together the best IT researchers, leading edge users and key IT technology providers to support consolidated, technically sound, integrated and strategically positioned research towards solutions for next generation Enterprise Computing. Web services, the Semantic Web and Service Oriented Computing are fast emerging new disciplines with far reaching impacts. EII will contribute to their growth and to their practical deployment in Australia and beyond. The establishment of EII network will dramatically add value to already supported but often fragmented research projects.Read moreRead less
ARC Research Network in Enterprise Information Infrastructure. EII targets consolidated research towards the comprehensive development & establishment of advanced information infrastructures. Its prime purpose is to provide a forum for intellectual exchange by diverse yet complementary research groups, to address the fundamental research problems faced by scientific & business communities when dealing with deployment of information technology to globally distributed, and data intensive environme ....ARC Research Network in Enterprise Information Infrastructure. EII targets consolidated research towards the comprehensive development & establishment of advanced information infrastructures. Its prime purpose is to provide a forum for intellectual exchange by diverse yet complementary research groups, to address the fundamental research problems faced by scientific & business communities when dealing with deployment of information technology to globally distributed, and data intensive environments. EII will address 3 tightly coupled research themes: Ability to interoperate across existing heterogenous platforms & applications; Efficient processing of very large data sets; Technology adoption & impact. Generic results will be applicable to e-science and large business information systems installations.Read moreRead less
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE100100111
Funder
Australian Research Council
Funding Amount
$150,000.00
Summary
A database for Australian optical astronomy. We will build an astronomy data facility to provide database facilities to analyse data from three major Australian optical astronomy projects, SkyMapper, WiggleZ and GAMA. The facility will provide efficient analysis tools not only for the researchers immediately involved with the projects, but, for the entire national and international astronomical community when the data become public. Long-term maintenance of the facility is vital, so the facilit ....A database for Australian optical astronomy. We will build an astronomy data facility to provide database facilities to analyse data from three major Australian optical astronomy projects, SkyMapper, WiggleZ and GAMA. The facility will provide efficient analysis tools not only for the researchers immediately involved with the projects, but, for the entire national and international astronomical community when the data become public. Long-term maintenance of the facility is vital, so the facility will be standards-compliant and stable to facilitate long-term support. For this reason the facility will be based at the ANU Supercomputer Facility (ANUSF) to leverage substantial expertise in this area and to provide long-term operations support.Read moreRead less
TraitCapture: Genomic modelling for plant phenomics under environmental stress. This project aims to develop software to integrate new hyper-spectral and 3D growth models of plant phenomics with population genomics to identify heritable developmental traits across varied environments. Genome wide association studies aim to then be used to identify causal genes. Functional structural plant models incorporating genetic variation will be used to predict growth under simulated stress environments. ....TraitCapture: Genomic modelling for plant phenomics under environmental stress. This project aims to develop software to integrate new hyper-spectral and 3D growth models of plant phenomics with population genomics to identify heritable developmental traits across varied environments. Genome wide association studies aim to then be used to identify causal genes. Functional structural plant models incorporating genetic variation will be used to predict growth under simulated stress environments. The research team unites international industry, the Australian Plant Phenomics Facility, and university statistical geneticists. TraitCapture software will use open standards applicable to both controlled and field environments enabling plant breeders to pre-select adaptive traits to increase crop productivity under environmental stress.Read moreRead less
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE110100049
Funder
Australian Research Council
Funding Amount
$600,000.00
Summary
Establishment of the Australian data archive: an integrated research facility for the social sciences and humanities. The Australian data archive will enable Australia's leading researchers to address complex social, economic and environmental problems, leading to the development of evidence based policy. The archive will have an open access policy which will ensure that the general public, media and government and non-government agencies are able to examine the data used by researchers to arriv ....Establishment of the Australian data archive: an integrated research facility for the social sciences and humanities. The Australian data archive will enable Australia's leading researchers to address complex social, economic and environmental problems, leading to the development of evidence based policy. The archive will have an open access policy which will ensure that the general public, media and government and non-government agencies are able to examine the data used by researchers to arrive at their conclusions.Read moreRead less