Creating the social genome: Advanced techniques for linking dynamic data. This project aims to develop novel efficient and effective models and techniques that enable record linkage of large dynamic databases while preserving the privacy of sensitive personal data. Social genomes are the digital footprints of our society. They are the basis of population informatics, which is revolutionising how researchers in various domains conduct studies, governments plan services and expenditures, and busin ....Creating the social genome: Advanced techniques for linking dynamic data. This project aims to develop novel efficient and effective models and techniques that enable record linkage of large dynamic databases while preserving the privacy of sensitive personal data. Social genomes are the digital footprints of our society. They are the basis of population informatics, which is revolutionising how researchers in various domains conduct studies, governments plan services and expenditures, and businesses advertise and interact with their customers. A core requirement of population informatics is the linking of large dynamic databases that contain details about people from diverse sources. The expected outcomes of this project will provide novel solutions to the challenges of population informatics faced by Australian organisations.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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