Quantum Generative Diffusion Models for Molecular Research. This project will devise quantum generative diffusion models to equip classical counterparts with the ability to harness quantum data that naturally arise in molecular research. Theoretical foundations for analysing fast sampling methods with the help of inductive bias regarding the input data and employed circuits will validate efficient quantum generative diffusion models that have training and sampling advantages over classical count ....Quantum Generative Diffusion Models for Molecular Research. This project will devise quantum generative diffusion models to equip classical counterparts with the ability to harness quantum data that naturally arise in molecular research. Theoretical foundations for analysing fast sampling methods with the help of inductive bias regarding the input data and employed circuits will validate efficient quantum generative diffusion models that have training and sampling advantages over classical counterparts. Outcomes include applications in molecular conformation generation, compound screening, and drug design. The innovative research will significantly benefit Australia’s science, industry and health, and will maintain Australia’s global leading role in quantum machine learning and molecular research.Read moreRead less
Optimising students’ academic trajectories: The role of growth (‘personal best’) goals. Too many students fail to reach their academic potential and, as a result, they risk being systematically denied a sense of academic ‘success’ and progress. Through a focus on academic growth (and ‘personal bests’), this research project traverses complex terrain to identify the role of growth goals and growth goal setting in students’ academic trajectories. It also tackles methodological challenges that have ....Optimising students’ academic trajectories: The role of growth (‘personal best’) goals. Too many students fail to reach their academic potential and, as a result, they risk being systematically denied a sense of academic ‘success’ and progress. Through a focus on academic growth (and ‘personal bests’), this research project traverses complex terrain to identify the role of growth goals and growth goal setting in students’ academic trajectories. It also tackles methodological challenges that have impeded research progress in this compelling area. Through strategic international and institutional links, the research program will identify innovative approaches to academic growth and growth goals that will significantly assist pedagogy and psychology aimed at optimising students’ academic potential.Read moreRead less
Solving the inert knowledge problem. A central goal of education is for students to transfer what they learn to new contexts or problems. Indeed, expert reasoning is often characterised by seeing the deep structural commonalities across seemingly disparate situations. However, the knowledge students acquire is notoriously inert, tied to the specifics of the learning examples. This project aims to move towards solving 'the inert knowledge problem' by investigating how humans learn concepts define ....Solving the inert knowledge problem. A central goal of education is for students to transfer what they learn to new contexts or problems. Indeed, expert reasoning is often characterised by seeing the deep structural commonalities across seemingly disparate situations. However, the knowledge students acquire is notoriously inert, tied to the specifics of the learning examples. This project aims to move towards solving 'the inert knowledge problem' by investigating how humans learn concepts defined by abstract relational structure, and by designing educational applications that enhance the use of relational learning mechanisms in students with a wide range of cognitive abilities.Read moreRead less
Excellent researchers: Using learner profiles to enhance research learning. Recent evidence concerning metacognitive learning and affect reveals that research degree candidates are a diverse group of learners, and little is known about explaining wasteful attrition, stress and delays in progress. Such a study is essential, especially given the growth in research degrees, new transitional pathways, diversity in candidate backgrounds and chronic high attrition. This longitudinal study applies new ....Excellent researchers: Using learner profiles to enhance research learning. Recent evidence concerning metacognitive learning and affect reveals that research degree candidates are a diverse group of learners, and little is known about explaining wasteful attrition, stress and delays in progress. Such a study is essential, especially given the growth in research degrees, new transitional pathways, diversity in candidate backgrounds and chronic high attrition. This longitudinal study applies new findings about doctoral learning profiles in a direct intervention (DOCLearnPro) that targets individual differences across students in doctoral and master’s degrees to improve learning outcomes significantly and contribute theoretically, methodologically and substantively in order to advance understanding of researcher development.Read moreRead less
Transforming primary teachers' representational practices: effects on students' scientific reasoning and discourse within contemporary sciences. Training teachers to appropriately represent and communicate scientific information is critically important for promoting scientific thinking and learning in students. This research is critical to securing Australia's future interests in developing new and emerging frontier science and technologies through the engagement and retention of students.
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
Discrimination learning in humans: Associative and attentional mechanisms. This project offers three major benefits: (1) Australian researchers excel in cognitive neuroscience, learning and psychopharmacology, areas based largely on animal models of human cognition. This project contributes to these areas by specifying the relationship between animal learning and human cognition; (2) the project enhances Australia's international reputation in these areas via its collaboration with a scientist ....Discrimination learning in humans: Associative and attentional mechanisms. This project offers three major benefits: (1) Australian researchers excel in cognitive neuroscience, learning and psychopharmacology, areas based largely on animal models of human cognition. This project contributes to these areas by specifying the relationship between animal learning and human cognition; (2) the project enhances Australia's international reputation in these areas via its collaboration with a scientist of Geoff Hall's stature; it also offers students outstanding research training and international exposure; (3) given Chris Mitchell's industry experience and the relevance of this work to advertising/marketing, this project will generate knowledge relevant to, and possible future collaborations with, Australian industries.Read moreRead less
Evaluating models of category learning that use general feature-based representations. Three competing models of human category learning will be evaluated by comparing their behaviour to human performance on an experimental task where each model makes qualitatively different predictions. A series of theoretical and algorithmic advances will be undertaken to ensure each of the category learning models uses the same feature-based representation. Because the three models propose very different lear ....Evaluating models of category learning that use general feature-based representations. Three competing models of human category learning will be evaluated by comparing their behaviour to human performance on an experimental task where each model makes qualitatively different predictions. A series of theoretical and algorithmic advances will be undertaken to ensure each of the category learning models uses the same feature-based representation. Because the three models propose very different learning processes, their comparison will give insight into the basic cognitive process of categorisation. The algorithms for generating feature representations and modelling human category learning will also have potential for application in data visualisation and information handling systems.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
Developing interdisciplinary expertise in universities. This project aims to create a strong integrative research foundation to explain how university researchers and students develop the expertise needed to work in interdisciplinary teams and how this development can be enhanced. It combines three perspectives investigating: how research and innovation communities create interdisciplinary knowledge, how interdisciplinary teams learn to function effectively and the personal resourcefulness that ....Developing interdisciplinary expertise in universities. This project aims to create a strong integrative research foundation to explain how university researchers and students develop the expertise needed to work in interdisciplinary teams and how this development can be enhanced. It combines three perspectives investigating: how research and innovation communities create interdisciplinary knowledge, how interdisciplinary teams learn to function effectively and the personal resourcefulness that enables individuals to participate in interdisciplinary work. The outcomes will provide a much better understanding of the qualities that help individuals and groups to work productively across disciplinary boundaries. They will be used to create better strategies for supporting interdisciplinary learningRead moreRead less