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Current Selection
Status : Active
Scheme : Discovery Projects
Australian State/Territory : ACT
Research Topic : learning difficulty
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  • Active Funded Activity

    Discovery Projects - Grant ID: DP240101848

    Funder
    Australian Research Council
    Funding Amount
    $510,000.00
    Summary
    Generative Visual Pre-training on Unlabelled Big Data. This project aims to develop a generative visual pre-training of large-scale deep neural networks on unlabelled big data. Developing pre-trained visual models that are accurate, robust, and efficient for downstream tasks is a keystone of modern computer vision, but it poses challenges and knowledge gaps to existing unsupervised representation learning. Expected outcomes include new theories and algorithms for unsupervised visual pre-training .... Generative Visual Pre-training on Unlabelled Big Data. This project aims to develop a generative visual pre-training of large-scale deep neural networks on unlabelled big data. Developing pre-trained visual models that are accurate, robust, and efficient for downstream tasks is a keystone of modern computer vision, but it poses challenges and knowledge gaps to existing unsupervised representation learning. Expected outcomes include new theories and algorithms for unsupervised visual pre-training, which are anticipated to deepen our understanding of visual representation and make it easier to build and deploy computer vision applications and services. Examples of benefits include modernising machines in manufacturing and farming with visual intelligence.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP230101176

    Funder
    Australian Research Council
    Funding Amount
    $420,000.00
    Summary
    Exploiting Geometries of Learning for Fast, Adaptive and Robust AI. This project aims to uniquely exploit geometric manifolds in deep learning to advance the frontier of Artificial Intelligence (AI) research and applications in cybersecurity and general cognitive tasks. It expects to develop new theories, algorithms, tools, and technologies for machine learning systems that are fast, adaptive, lifelong and robust, even with limited supervision. Expected outcomes will enhance Australia's capabili .... Exploiting Geometries of Learning for Fast, Adaptive and Robust AI. This project aims to uniquely exploit geometric manifolds in deep learning to advance the frontier of Artificial Intelligence (AI) research and applications in cybersecurity and general cognitive tasks. It expects to develop new theories, algorithms, tools, and technologies for machine learning systems that are fast, adaptive, lifelong and robust, even with limited supervision. Expected outcomes will enhance Australia's capability and competitiveness in AI, and deliver robust and trustworthy learning technology. The project should provide significant benefits not only in advancing scientific and translational knowledge but also in accelerating AI innovations, safeguarding cyberspace, and reducing the burden on defence expenses in Australia.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP170101755

    Funder
    Australian Research Council
    Funding Amount
    $541,000.00
    Summary
    Equity and spatial reasoning in students’ mathematics development. The project aims to understand the influence of Spatial-Reasoning on school mathematics. Spatial-Reasoning skills are a significant predictor of achievement in mathematics, and will become increasingly necessary in digital and dynamic environments. Opportunities for disadvantaged students to develop such reasoning skills are limited; they are typically not taught in schools. The project investigates the role and nature of Spatial .... Equity and spatial reasoning in students’ mathematics development. The project aims to understand the influence of Spatial-Reasoning on school mathematics. Spatial-Reasoning skills are a significant predictor of achievement in mathematics, and will become increasingly necessary in digital and dynamic environments. Opportunities for disadvantaged students to develop such reasoning skills are limited; they are typically not taught in schools. The project investigates the role and nature of Spatial-Reasoning in students’ mathematics development; and substantiates the long-term effect of a spatial learning programme on educationally disadvantaged students’ mathematics performance and reasoning. This project is expected to improve disadvantaged students’ spatial reasoning and mathematics skills and their life opportunities.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP200102274

    Funder
    Australian Research Council
    Funding Amount
    $390,000.00
    Summary
    3D Vision Geometric Optimisation in Deep Learning. This project aims to develop a methodology for integrating the algorithms of 3D Vision Geometry and Optimization into the framework of Machine Learning and demonstrate the wide applicability of the new methods on a variety of challenging fundamental problems in Computer Vision. These include 3D geometric scene understanding, and estimation and prediction of human 2D/3D pose and activity. Applications of this technology are to be found in Intell .... 3D Vision Geometric Optimisation in Deep Learning. This project aims to develop a methodology for integrating the algorithms of 3D Vision Geometry and Optimization into the framework of Machine Learning and demonstrate the wide applicability of the new methods on a variety of challenging fundamental problems in Computer Vision. These include 3D geometric scene understanding, and estimation and prediction of human 2D/3D pose and activity. Applications of this technology are to be found in Intelligent Transportation, Environment Monitoring, and Augmented Reality, applicable in smart-city planning and medical applications such as computer-enhanced surgery. The goal is to build Australia's competitive advantage in the forefront of ICT research and technology innovation.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP220101336

    Funder
    Australian Research Council
    Funding Amount
    $488,142.00
    Summary
    Historical frontier violence: drivers, legacy and the role of truth-telling. This project aims to build data to identify the historical factors that incited frontier violence; quantify the legacy on communities today and conduct fieldwork to understand how historical trauma is transmitted across generations. This project expects to develop new knowledge on the circumstances and legacy of settlement and the origins of gaps in life prospects between Indigenous and non-Indigenous Australians. Our e .... Historical frontier violence: drivers, legacy and the role of truth-telling. This project aims to build data to identify the historical factors that incited frontier violence; quantify the legacy on communities today and conduct fieldwork to understand how historical trauma is transmitted across generations. This project expects to develop new knowledge on the circumstances and legacy of settlement and the origins of gaps in life prospects between Indigenous and non-Indigenous Australians. Our expectation is that this will increase public acceptance of the circumstances of settlement and the need to make amends. This project should help increase public support for truth-telling and better relations between Indigenous and non-Indigenous Australians, a vital step towards reconciliation and healing the nation.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP200103760

    Funder
    Australian Research Council
    Funding Amount
    $405,000.00
    Summary
    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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