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Near Unsupervised Learning for Early Discovery of Novel Patterns: Methods, Scalability and Label Dependability. This project aims to predict the unknown class labels using the existing small number of class labels. The outcomes of the project have direct relevance to the economy, environment, energy and health sectors due to the abundance of data coming out of these areas. For example, if an oncogene, a gene that can cause cancer when mutated can be found using data with only few labels and a la ....Near Unsupervised Learning for Early Discovery of Novel Patterns: Methods, Scalability and Label Dependability. This project aims to predict the unknown class labels using the existing small number of class labels. The outcomes of the project have direct relevance to the economy, environment, energy and health sectors due to the abundance of data coming out of these areas. For example, if an oncogene, a gene that can cause cancer when mutated can be found using data with only few labels and a large amount of unlabelled data, the costs and time needed for lab experimentation can be greatly reduced enabling pharmaceutical companies to develop corresponding medicines quicker. It will not only save more lives but also generates millions of dollars of income.Read moreRead less
Interpretable Behaviour Analysis with External Structured Knowledge. This project aims to develop novel interpretable neural models for predictive analytics tasks on human behaviour, operating on sequence behaviour data associated with external supportive structured knowledge. It is expected to present theoretical foundations for robust representation learning on heterogeneous behaviour data and interpretable machine reasoning models, which can support a broad scope of intelligent systems. Expec ....Interpretable Behaviour Analysis with External Structured Knowledge. This project aims to develop novel interpretable neural models for predictive analytics tasks on human behaviour, operating on sequence behaviour data associated with external supportive structured knowledge. It is expected to present theoretical foundations for robust representation learning on heterogeneous behaviour data and interpretable machine reasoning models, which can support a broad scope of intelligent systems. Expected outcomes will be a next-generation interpretable behaviour analysis system with versatile abilities to reason over various data structures and provide a high-level interpretability about its reasoning procedure. The benefits will span the research and industry sectors, e.g., retail, healthcare, service provider.Read moreRead less
Responding to requests and situations in assistive computer systems - a decision-theoretic approach. This project aims to enable computer agents to respond appropriately to people's spoken requests and circumstances (e.g., ask questions or perform actions). This project will investigate computational models for response generation, which will be implemented in assistive computer systems, thus enabling people to interact more easily with these systems.
Inferring driver behaviours, intent and risk in complex traffic scenarios. This project intends to develop methods to evaluate risk during driving. The next generation of vehicles will be fitted with sophisticated perception and egocentric information. This will be combined with inter-vehicle communication enabling cooperative safety, used in conjunction with intelligent infrastructure. This technology is expected to be mandated in the United States starting from 2017. This project plans to deve ....Inferring driver behaviours, intent and risk in complex traffic scenarios. This project intends to develop methods to evaluate risk during driving. The next generation of vehicles will be fitted with sophisticated perception and egocentric information. This will be combined with inter-vehicle communication enabling cooperative safety, used in conjunction with intelligent infrastructure. This technology is expected to be mandated in the United States starting from 2017. This project plans to develop unsupervised learning algorithms to infer high-level driver behaviours, intent and contextual information to automatically evaluate levels of risk under complex driving scenarios. It plans to validate the results using naturalistic driving datasets taken in large-scale deployments around the world. This innovation may improve automotive safety and facilitate the deployment of autonomous vehicles.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE160100007
Funder
Australian Research Council
Funding Amount
$303,000.00
Summary
The Future of Urban Routing and Navigation. This project aims to develop new efficient techniques for mixed-initiative routing in large transportation networks. Current state-of-the-art techniques for real-world journey planning take user requirements as input and generate a few proposed journeys as output. However, the most useful decision-support systems are mixed-initiative: the Information Technology (IT) system and user work together to find the best decisions. In the context of journey pla ....The Future of Urban Routing and Navigation. This project aims to develop new efficient techniques for mixed-initiative routing in large transportation networks. Current state-of-the-art techniques for real-world journey planning take user requirements as input and generate a few proposed journeys as output. However, the most useful decision-support systems are mixed-initiative: the Information Technology (IT) system and user work together to find the best decisions. In the context of journey planning, interaction with the user is needed to find the best combination of private, public and active transportation; understand trade-offs between cost, starting time, journey time, convenience and reliability; and react to delays and disruptions. This project aims to develop dynamic decision-support systems that will help travellers reach their destinations cheaper, faster and more conveniently.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE200101310
Funder
Australian Research Council
Funding Amount
$426,918.00
Summary
Dimension-reduced Reinforcement Learning for Large-scale Fleet Management. This project aims to address the problems in large-scale fleet management to ensure the efficiency of tomorrow’s transportation models, such as on-demand ride-hailing and mobility-as-a-service. The expected outcomes of this project include improved techniques for optimising the utility of large fleets of vehicles, and particularly robust dimension-reduced reinforcement learning algorithms that are capable of handling the ....Dimension-reduced Reinforcement Learning for Large-scale Fleet Management. This project aims to address the problems in large-scale fleet management to ensure the efficiency of tomorrow’s transportation models, such as on-demand ride-hailing and mobility-as-a-service. The expected outcomes of this project include improved techniques for optimising the utility of large fleets of vehicles, and particularly robust dimension-reduced reinforcement learning algorithms that are capable of handling the complex dynamics of supply and demand in transportation. The results should advance both research and technology in academia and the transportation industry and will also provide significant benefits to Australia and the international community by enhancing the energy-efficiency of and access to the mobility of the future.Read moreRead less
Defense against adversarial attacks on deep learning in computer vision. Computer vision applications rely heavily on deep learning, which is highly vulnerable to being fooled by adding subtle perturbations to object/image textures that are imperceptible to humans. This project aims to develop defense mechanisms to detect and remove adversarial patterns from the input images. The project expects to advance knowledge in understanding the vulnerabilities of deep learning, and to design deep learni ....Defense against adversarial attacks on deep learning in computer vision. Computer vision applications rely heavily on deep learning, which is highly vulnerable to being fooled by adding subtle perturbations to object/image textures that are imperceptible to humans. This project aims to develop defense mechanisms to detect and remove adversarial patterns from the input images. The project expects to advance knowledge in understanding the vulnerabilities of deep learning, and to design deep learning architectures that are inherently robust. The outcomes of this project will increase the security and reliability of computer vision by detecting, reporting and nullifying such attacks and will benefit the general public and industry on many fronts.Read moreRead less
Robust and Explainable 3D Computer Vision. Computer vision is increasingly relying on deep learning which is fragile, opaque and fails catastrophically without warning. This project aims to address these problems by developing new theory in graph representation of 3D geometric and image data, hierarchical graph simplification and novel modules designed specifically for deep learning over geometric graphs. Using these modules, it aims to design graph convolutional network architectures for self-s ....Robust and Explainable 3D Computer Vision. Computer vision is increasingly relying on deep learning which is fragile, opaque and fails catastrophically without warning. This project aims to address these problems by developing new theory in graph representation of 3D geometric and image data, hierarchical graph simplification and novel modules designed specifically for deep learning over geometric graphs. Using these modules, it aims to design graph convolutional network architectures for self-supervised learning that are robust to failures and provide explainable decisions for object detection and scene segmentation. The outcomes are expected to advance theory in robust deep learning and benefit 3D mapping, surveying, infrastructure monitoring, transport and robotics industries.Read moreRead less
Robust Configuration of Evolutionary Algorithms. The purpose of this project is to develop an intelligent framework for the robust configuration of evolutionary algorithms. This research is driven by the fact that the current design of evolutionary algorithms is sub-optimal and ineffective for many problem domains. In the proposed framework, a configuration is evolved while the algorithm is running for problem solving to ensure robust design. Its scientific outcomes are expected to include a nov ....Robust Configuration of Evolutionary Algorithms. The purpose of this project is to develop an intelligent framework for the robust configuration of evolutionary algorithms. This research is driven by the fact that the current design of evolutionary algorithms is sub-optimal and ineffective for many problem domains. In the proposed framework, a configuration is evolved while the algorithm is running for problem solving to ensure robust design. Its scientific outcomes are expected to include a novel framework for the automated design of algorithms and new techniques for exploiting assumptions in algorithmic design that may have been overlooked. Expected practical outcomes include the provision of a robust problem-solving tool, strong research training and high-impact publications.Read moreRead less
Robust evolutionary analytics for changing and uncertain environments. This project aims to develop a novel framework for solving planning problems in dynamic environments with uncertainties. Current methods treat these conditions as two discrete problems. In the proposed framework, three algorithms will be developed and integrated to generate robust solutions for planning under dynamic changes with uncertainties. The intended outcomes include a novel framework with new techniques, developed by ....Robust evolutionary analytics for changing and uncertain environments. This project aims to develop a novel framework for solving planning problems in dynamic environments with uncertainties. Current methods treat these conditions as two discrete problems. In the proposed framework, three algorithms will be developed and integrated to generate robust solutions for planning under dynamic changes with uncertainties. The intended outcomes include a novel framework with new techniques, developed by exploiting the assumptions of existing methodologies. Practical outcomes will include a robust planning tool.Read moreRead less