Online Learning for Large Scale Structured Data in Complex Situations. Online Learning (OL) is the process of predicting answers for a sequence of questions. OL has enjoyed much attention in recent years due to its natural ability of processing large scale non-structured data and adapting to a changing environment. However, OL has three weaknesses: it does not scale for structured data; it often assumes that all of the data are equally important; it often considers that all of the data are compl ....Online Learning for Large Scale Structured Data in Complex Situations. Online Learning (OL) is the process of predicting answers for a sequence of questions. OL has enjoyed much attention in recent years due to its natural ability of processing large scale non-structured data and adapting to a changing environment. However, OL has three weaknesses: it does not scale for structured data; it often assumes that all of the data are equally important; it often considers that all of the data are complete and noise-free. These weaknesses limit its utility, because real data such as those that must be analysed in processing social networks, fraud detection do not satisfy the restrictions. The aim of this project is to develop theoretical and practical advances in OL that overcome the existing weaknesses.Read moreRead less
Making Meta-learning Generalised . This project aims to develop novel machine learning techniques, termed generalised meta-learning, to make machines better utilise past experience to solve new tasks with few data. It expects to reduce the undesirable dependence of current machine learning on labelled data and significantly expand its application scope. Expected outcomes of the project consist of new theoretical results on meta-learning and a set of innovative algorithms that can support the bui ....Making Meta-learning Generalised . This project aims to develop novel machine learning techniques, termed generalised meta-learning, to make machines better utilise past experience to solve new tasks with few data. It expects to reduce the undesirable dependence of current machine learning on labelled data and significantly expand its application scope. Expected outcomes of the project consist of new theoretical results on meta-learning and a set of innovative algorithms that can support the building of next generation of computer vision systems to work in open and dynamic environments. This should be able to produce solid benefits to the science, society, and economy of Australian via the application of these advanced intelligent systems.Read moreRead less
Switching Dynamics Approach for Distributed Global Optimisation . This project aims to create a breakthrough switching dynamics approach and new technology to speed up finding optimal solutions. It will develop a distributed switching dynamics based optimisation scheme for global optimisation problems in industrial big-data environments where timely decision making is required. It will result in a practical technology for industry optimisation problems such as economic energy dispatch in smart g ....Switching Dynamics Approach for Distributed Global Optimisation . This project aims to create a breakthrough switching dynamics approach and new technology to speed up finding optimal solutions. It will develop a distributed switching dynamics based optimisation scheme for global optimisation problems in industrial big-data environments where timely decision making is required. It will result in a practical technology for industry optimisation problems such as economic energy dispatch in smart grids and optimal charging and discharging tasks in a large network of electric vehicles, helping Australian power industry improve efficiency and security, as well as training the next generation scientists and engineers for Australia in this emerging field.Read moreRead less
Multiobjective Memetic Algorithms for Multi-task Symbolic Regression. This project aims at developing the new generation of symbolic regression methods using a yet unexplored way to represent mathematical functions. We will use memetic algorithms to create mathematical models for symbolic regression. Our memetic computing approach will be data-driven and will use multi-objective optimization and multi-task evolutionary computation for symbolic regression, addressing a core need of many areas of ....Multiobjective Memetic Algorithms for Multi-task Symbolic Regression. This project aims at developing the new generation of symbolic regression methods using a yet unexplored way to represent mathematical functions. We will use memetic algorithms to create mathematical models for symbolic regression. Our memetic computing approach will be data-driven and will use multi-objective optimization and multi-task evolutionary computation for symbolic regression, addressing a core need of many areas of science and technology. A large number of datasets will be investigated to benchmark the new methods. The expected outcomes will help support our national priorities with new data analytic capabilities. With a strong and interdisciplinary team in three continents, the project will attract international collaboration. Read moreRead less
Automated analysis of multi-modal medical data using deep belief networks. This project will develop an improved breast cancer computer-aided diagnosis (CAD) system that incorporates mammography, ultrasound and magnetic resonance imaging. This system will be based on recently developed deep learning techniques, which have the capacity to process multi-modal data in a unified and optimal manner. The advantage of this technique is that it is able to automatically learn both the relevant features t ....Automated analysis of multi-modal medical data using deep belief networks. This project will develop an improved breast cancer computer-aided diagnosis (CAD) system that incorporates mammography, ultrasound and magnetic resonance imaging. This system will be based on recently developed deep learning techniques, which have the capacity to process multi-modal data in a unified and optimal manner. The advantage of this technique is that it is able to automatically learn both the relevant features to analyse in each modality and the hidden relationships between them. The use of deep belief networks has produced promising results in several fields, such as speech recognition, and so this project believes that our approach has the potential to improve both the sensitivity and specificity of breast cancer detection.Read moreRead less
Deep reinforcement learning for discovering and visualising biomarkers. This project aims to develop novel methods for discovering and visualising optimal bio-markers from chest computed tomography images based on extensions of recently developed deep reinforcement learning techniques. The extensions proposed in this project will advance medical image analysis by allowing an efficient analysis of large dimensionality inputs in their original high resolution. In addition, this project will be the ....Deep reinforcement learning for discovering and visualising biomarkers. This project aims to develop novel methods for discovering and visualising optimal bio-markers from chest computed tomography images based on extensions of recently developed deep reinforcement learning techniques. The extensions proposed in this project will advance medical image analysis by allowing an efficient analysis of large dimensionality inputs in their original high resolution. In addition, this project will be the first approach capable of discovering previously unknown biomarkers associated with important clinical outcomes. The project will validate the approach on a real-world case study data set concerning the prediction of five-year survival of chronic disease.Read moreRead less
Interacting with visualisations of extremely large graph structures on large displays. The latest technological progressions have delivered very large data sets that can be modelled as graphs or networks. Examples include: social networks, biological data, and software structures. This project will develop techniques to allow users to visualise the graphs in the entirety and directly interact with data.