Toward Human-guided Safe Reinforcement Learning in the Real World. This project aims to investigate human-guided safe reinforcement learning (RL). Safe RL is an important topic that could enable real applications of RL systems by addressing safety constraints. Existing safe RL assumes the availability of specified safety constraints in mathematical or logical forms. This project proposes to study learning safety objectives from information provided directly by humans or indirectly via language m ....Toward Human-guided Safe Reinforcement Learning in the Real World. This project aims to investigate human-guided safe reinforcement learning (RL). Safe RL is an important topic that could enable real applications of RL systems by addressing safety constraints. Existing safe RL assumes the availability of specified safety constraints in mathematical or logical forms. This project proposes to study learning safety objectives from information provided directly by humans or indirectly via language models, and human-guided continuous correction for safety improvements. The established theories and developed algorithms will advance frontier technologies in AI and contribute to a wide range of real applications of safe RL, such as robotics and autonomous driving, bringing enormous social and economic benefits. Read moreRead less
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. Read moreRead less
Data Complexity and Uncertainty-Resilient Deep Variational Learning. Enterprise data present increasingly significant characteristics and complexities, such as multi-aspect, heterogeneous and hierarchical features and interactions, and evolving dependencies and multi-distributions. They continue to significantly challenge the state-of-the-art probabilistic and neural learning systems with limited to insufficient capabilities and capacity. This research aims to develop a theory of flexible deep v ....Data Complexity and Uncertainty-Resilient Deep Variational Learning. Enterprise data present increasingly significant characteristics and complexities, such as multi-aspect, heterogeneous and hierarchical features and interactions, and evolving dependencies and multi-distributions. They continue to significantly challenge the state-of-the-art probabilistic and neural learning systems with limited to insufficient capabilities and capacity. This research aims to develop a theory of flexible deep variational learning transforming new deep probabilistic models with flexible variational neural mechanisms for analytically explainable, complexity-resilient analytics of real-life data. The outcomes are expected to fill important knowledge gaps and lift critical innovation competencies in wide domains.Read moreRead less
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.Read moreRead less
Tracking towards a complete model of skilled reading comprehension. This project aims to promote the development of the first complete computational model of reading comprehension. Many computational models of sub-components of reading have been developed, but none fully explain the complex co-ordination of perceptual, attentional and cognitive processes required for successful comprehension. The project intends to use eye tracking studies to test and refine Über-Reader, a new computational mode ....Tracking towards a complete model of skilled reading comprehension. This project aims to promote the development of the first complete computational model of reading comprehension. Many computational models of sub-components of reading have been developed, but none fully explain the complex co-ordination of perceptual, attentional and cognitive processes required for successful comprehension. The project intends to use eye tracking studies to test and refine Über-Reader, a new computational model that aims to provide a complete account of the memory systems and cognitive processes involved in reading comprehension and how they differ with reading skill. The outcomes will advance understanding of the causes of success and failure in reading and contribute to diagnosing and remediating reading difficulties.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
Tracking the Flow of Perceptual Information Through Decision Networks. The choices we make define our lives. Despite exciting progress in neuroscience, we still don’t know how the inner workings of the brain give rise to simple decisions. This project brings together experts from diverse domains of computational neuroscience to investigate how our brains turn perceptual information into action. Together, we will develop new methods to track information flow through the brain during the decision ....Tracking the Flow of Perceptual Information Through Decision Networks. The choices we make define our lives. Despite exciting progress in neuroscience, we still don’t know how the inner workings of the brain give rise to simple decisions. This project brings together experts from diverse domains of computational neuroscience to investigate how our brains turn perceptual information into action. Together, we will develop new methods to track information flow through the brain during the decision making process. By doing so, we will develop a world-leading model of how the brain makes decisions, and also provide the broader scientific community with a set of exciting new tools for studying information processing in the brain.Read moreRead less
Planet Formation at Solar System Scales with the James Webb Space Telescope. Planetary systems like our own form within vast disks of primordial gas and dust around newborn stars. This project will observe such disks spanning a range of ages with the James Webb Space Telescope to reveal the detailed in-situ physics of planet-forming disks themselves. We will deliver the sharpest-ever infrared images in astronomy, exploiting the only Australian-designed instrument on the spacecraft: the Aperture ....Planet Formation at Solar System Scales with the James Webb Space Telescope. Planetary systems like our own form within vast disks of primordial gas and dust around newborn stars. This project will observe such disks spanning a range of ages with the James Webb Space Telescope to reveal the detailed in-situ physics of planet-forming disks themselves. We will deliver the sharpest-ever infrared images in astronomy, exploiting the only Australian-designed instrument on the spacecraft: the Aperture Masking Interferometer. This yields new physics for actively growing protoplanets, carved rings and gaps in disks, and gravitationally sculpted patterns of leftover cometary debris. Confronting state-of-the-art models with these data will immediately yield profound insights into planetary system formation, including our own.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
How people learn inhibitory associations. This project aims to combine insights from associative and cognitive theories to investigate how people acquire, represent and generalise knowledge about inhibitory, or preventative, relationships. The project intends to use novel methods to assess the inhibitory causal structures inferred by individual participants, expected to include direct outcome prevention, modulation of a causal relationship, and configural learning. This project should expand our ....How people learn inhibitory associations. This project aims to combine insights from associative and cognitive theories to investigate how people acquire, represent and generalise knowledge about inhibitory, or preventative, relationships. The project intends to use novel methods to assess the inhibitory causal structures inferred by individual participants, expected to include direct outcome prevention, modulation of a causal relationship, and configural learning. This project should expand our understanding of the mechanisms of human associative learning. The project should benefit and inform clinical interventions based on identifying and normalising maladaptive learned associations.Read moreRead less