A Generic Framework for Verifying Machine Learning Algorithms. This project aims to discover new ways to verify whether decisions made by Artificial Intelligence and Machine Learning algorithms are as per the specifications set by their designers and/or regulatory bodies. The project also provides new methods to align algorithm decisions when they are found to be non-abiding. The outcomes will include new machine learning theories and frameworks for algorithmic assurance. The significance of the ....A Generic Framework for Verifying Machine Learning Algorithms. This project aims to discover new ways to verify whether decisions made by Artificial Intelligence and Machine Learning algorithms are as per the specifications set by their designers and/or regulatory bodies. The project also provides new methods to align algorithm decisions when they are found to be non-abiding. The outcomes will include new machine learning theories and frameworks for algorithmic assurance. The significance of the project is that it will offer a crucial platform for certifying algorithms and thus benefit society and businesses in deciding the right Artificial Intelligence algorithms. Read moreRead less
Making sense of ambiguity: brain system interactions and visual uncertainty. This project aims to identify and characterise the interactions between brain regions underlying a fundamental process in visual perception: interpreting sensory input that is unclear or ambiguous. It will use two complementary neuroimaging techniques and cutting-edge analysis methods. The intended outcomes include new insights into a fundamental but poorly characterised aspect of brain function: how brain regions inter ....Making sense of ambiguity: brain system interactions and visual uncertainty. This project aims to identify and characterise the interactions between brain regions underlying a fundamental process in visual perception: interpreting sensory input that is unclear or ambiguous. It will use two complementary neuroimaging techniques and cutting-edge analysis methods. The intended outcomes include new insights into a fundamental but poorly characterised aspect of brain function: how brain regions interact, and advanced analysis methods with wide application. Expected benefits include important advances in knowledge that lay foundations for future study of neural disorders, international collaboration, and new methods placing Australia at the forefront of the international effort to understand the human brain. Read moreRead less
Unlocking Mass Mobile Video Analytics with Advanced Neural Memory Networks. This project will develop neural memory architectures and dense spatial-temporal bundle adjustment to predict movement, behaviour, and perform multi-sensor fusion across large asynchronous video feeds. This capability will allow us to better interrogate and analyse mass video information recorded from the vast number of smartphones, action cameras, and surveillance cameras which exist at public events of interest. Outcom ....Unlocking Mass Mobile Video Analytics with Advanced Neural Memory Networks. This project will develop neural memory architectures and dense spatial-temporal bundle adjustment to predict movement, behaviour, and perform multi-sensor fusion across large asynchronous video feeds. This capability will allow us to better interrogate and analyse mass video information recorded from the vast number of smartphones, action cameras, and surveillance cameras which exist at public events of interest. Outcomes include the ability to ingest multiple video feeds into a dense and dynamic 3D reconstruction for knowledge representation and discovery, and analysis of events and behaviour through new spatio-temporal analytic approaches. This will offer significant benefits for video forensic analysis, policing, and emergency response.Read moreRead less
How is information organised in the mind? Learning structured mental representations from data. One of the biggest questions in psychology is to understand the principles that the mind uses to organise information. This project is both a search for these underlying psychological laws, and an attempt to develop new statistical technologies and mathematical tools that can be used to organise information in applied settings.
Personalised Online Learning Analytics by Exploring Multilayer Graph Data . Learning analytics is becoming a significant factor in reducing the drop-out rate of students in online learning. The aim of this project is to develop a reliable, robust, real-time analysis system that automatically reveals multilayered relationships, evaluates students' learning performance, and generates a personal study plan through discovery. This project includes the design of novel algorithms for multilayer graph ....Personalised Online Learning Analytics by Exploring Multilayer Graph Data . Learning analytics is becoming a significant factor in reducing the drop-out rate of students in online learning. The aim of this project is to develop a reliable, robust, real-time analysis system that automatically reveals multilayered relationships, evaluates students' learning performance, and generates a personal study plan through discovery. This project includes the design of novel algorithms for multilayer graph processing, pattern recognition in learning activities, learning performance assessment, and personalised study plan recommendations. The success of this project will significantly enhance the success of online education both in Australia and worldwide and; hence, will save time, money and resources for end users.Read moreRead less
Nonparametric Machine Learning for Modern Data Analytics. This project intends to develop next-generation machine-learning methods to cope with the growing data deluge. Modern data analytics tasks need to interpret and derive values from complex, growing data. Intended outcomes of the project include new Bayesian nonparametric methods that can express arbitrary dependency amongst multiple, heterogeneous data sources with infinite model complexity, together with algorithms to perform inference an ....Nonparametric Machine Learning for Modern Data Analytics. This project intends to develop next-generation machine-learning methods to cope with the growing data deluge. Modern data analytics tasks need to interpret and derive values from complex, growing data. Intended outcomes of the project include new Bayesian nonparametric methods that can express arbitrary dependency amongst multiple, heterogeneous data sources with infinite model complexity, together with algorithms to perform inference and deduce knowledge from them; new Bayesian statistical inference for set-valued random variables that moves beyond vectors and matrices to enrich our analytics toolbox to deal with sets; and a new deterministic fast inference to meet with real-world demand.Read moreRead less
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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Discovery Early Career Researcher Award - Grant ID: DE200101610
Funder
Australian Research Council
Funding Amount
$403,398.00
Summary
Towards Explainable Multi-source Multivariate Time-series Analysis. The aim of this project is to build deep learning models with transparent reasoning behind the results that can be easily interpreted by humans. The research rests on translating pertinent knowledge from multiple sources of complex data containing event sequences into graph form and embedding those knowledge graphs into a sophisticated deep learning model. Such an accomplishment represents the next great advance in machine intel ....Towards Explainable Multi-source Multivariate Time-series Analysis. The aim of this project is to build deep learning models with transparent reasoning behind the results that can be easily interpreted by humans. The research rests on translating pertinent knowledge from multiple sources of complex data containing event sequences into graph form and embedding those knowledge graphs into a sophisticated deep learning model. Such an accomplishment represents the next great advance in machine intelligence and will lay the theoretical foundations for building intelligent analysis tools that truly work in tandem with people. The potential benefits to science, society, and the Australian economy, particularly in finance, sensor technologies, and emergency health services would be appreciable.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE190100626
Funder
Australian Research Council
Funding Amount
$393,000.00
Summary
Towards data-efficient future action prediction in the wild. This project aims to build state-of-the-art deep learning models to predict future actions in videos. The project expects to produce the next great step for machine intelligence, the potential to explore a handful of labelled examples to better understand, interpret and infer human actions. Expected outcomes of this project lay theoretical foundations for learning future action prediction in the wild scenario and build the next generat ....Towards data-efficient future action prediction in the wild. This project aims to build state-of-the-art deep learning models to predict future actions in videos. The project expects to produce the next great step for machine intelligence, the potential to explore a handful of labelled examples to better understand, interpret and infer human actions. Expected outcomes of this project lay theoretical foundations for learning future action prediction in the wild scenario and build the next generation of intelligent systems to accommodate limited supervision. This should benefit science, society, and the economy nationally through the applications of autonomous vehicles, sensor technologies, and cybersecurity.Read moreRead less