Stochastic Construction of Error Correcting Codes with Application to Digital Communications. Modern society would be unrecognisable without error correcting codes; mobile telephones, storage devices such as DVD's and high speed data communications simply would not exist. Yet most theoretical results on error correcting codes are asymptotic in nature and ignore computational complexity issues, that is, they are not representative of many real life situations. By building on recent breakthrough ....Stochastic Construction of Error Correcting Codes with Application to Digital Communications. Modern society would be unrecognisable without error correcting codes; mobile telephones, storage devices such as DVD's and high speed data communications simply would not exist. Yet most theoretical results on error correcting codes are asymptotic in nature and ignore computational complexity issues, that is, they are not representative of many real life situations. By building on recent breakthroughs in statistics and stochastic optimisation, this project will develop algorithms for designing optimised error correcting codes subject to realistic finite data length and computational complexity constraints. Successful outcomes will lead to enhanced data communications and storage, greatly benefiting industry and consumers alike.
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Discovery Early Career Researcher Award - Grant ID: DE140100772
Funder
Australian Research Council
Funding Amount
$393,414.00
Summary
Response Time Constraints on Category Learning. Theories of associative learning and decision-making are among the most mathematically well developed in psychology. However, theories of learning do not account for the time course of decision-making, and theories of decision-making do not account for how decision-relevant information is learned. This project will develop an integrated theoretical framework linking core principles of associative learning theories with sequential sampling models of ....Response Time Constraints on Category Learning. Theories of associative learning and decision-making are among the most mathematically well developed in psychology. However, theories of learning do not account for the time course of decision-making, and theories of decision-making do not account for how decision-relevant information is learned. This project will develop an integrated theoretical framework linking core principles of associative learning theories with sequential sampling models of the time course of decision-making. The new theory will provide a quantitative account of how incremental associative learning processes drive changes in cognitive representations that, in turn, account for known changes in the time course of decision-making.Read moreRead less
The dog that didn't bark: a Bayesian account of reasoning from censored data. This project aims to develop and test a new computational theory of inductive reasoning. Inductive reasoning involves extending knowledge from known to novel instances, and is a central component of intelligent behaviour. This project will address the cognitive mechanisms that allow people to draw inferences based on both observed and censored evidence. The project intends to test the model through an extensive program ....The dog that didn't bark: a Bayesian account of reasoning from censored data. This project aims to develop and test a new computational theory of inductive reasoning. Inductive reasoning involves extending knowledge from known to novel instances, and is a central component of intelligent behaviour. This project will address the cognitive mechanisms that allow people to draw inferences based on both observed and censored evidence. The project intends to test the model through an extensive program of experimental investigation and computational modelling. The anticipated benefits include an enhanced understanding of human inference, especially in domains such as the evaluation of forensic or financial evidence, where data censoring is common.Read moreRead less
Where do inductive biases come from? A Bayesian investigation. This project aims to investigate the origin of our thinking and learning biases using state-of-the-art mathematical models and sophisticated experimental designs. Expected outcomes include bridging the gap between human and machine learning by pairing mathematical modelling with experimental work, forming a necessary step toward the development of machine systems that can reason like people do. This will provide significant benefits ....Where do inductive biases come from? A Bayesian investigation. This project aims to investigate the origin of our thinking and learning biases using state-of-the-art mathematical models and sophisticated experimental designs. Expected outcomes include bridging the gap between human and machine learning by pairing mathematical modelling with experimental work, forming a necessary step toward the development of machine systems that can reason like people do. This will provide significant benefits such as understanding how people operate so effectively in real environments, when even the most powerful computers struggle to handle the complexities of everyday learning problems.Read moreRead less
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE0347079
Funder
Australian Research Council
Funding Amount
$160,000.00
Summary
Surface and strain measurement facilities for the investigation of intelligent CAD approaches. The basis of machine learning approaches is the ability to learn or train a system from data gathered through experiments or experience. A major short coming in the development and application of such methods is the lack of good quantitative data. Here we propose the acquisition of dimensional and strain measurement facilities that will allow the investigation of such methods in the context of manufact ....Surface and strain measurement facilities for the investigation of intelligent CAD approaches. The basis of machine learning approaches is the ability to learn or train a system from data gathered through experiments or experience. A major short coming in the development and application of such methods is the lack of good quantitative data. Here we propose the acquisition of dimensional and strain measurement facilities that will allow the investigation of such methods in the context of manufacturing - in particular sheet metal components for the automotive industry. The facilities will enable a database of dimensional and strain information to be established in support of related manufacturing R&D projects.Read moreRead less
Learning from others: Inductive reasoning based on human-generated data. Most of the data we see every day, from politics to gossip, comes from other people. Making inferences about such data is difficult because the people who provided it may have biases or limitations in their knowledge that we do not know about and must figure out. This project uses a series of experiments tied to normative computational models of social reasoning to explore how people solve this problem. This work has the po ....Learning from others: Inductive reasoning based on human-generated data. Most of the data we see every day, from politics to gossip, comes from other people. Making inferences about such data is difficult because the people who provided it may have biases or limitations in their knowledge that we do not know about and must figure out. This project uses a series of experiments tied to normative computational models of social reasoning to explore how people solve this problem. This work has the potential to make a major impact in understanding how information is understood and shared, especially when it is about topics that people lack firsthand knowledge about, like climate change. The computational models also have applications to the development of expert systems upon which our information economy relies.Read moreRead less
Practice-based Systematized Nomenclature of Medicine (SNOMED) concept learning for drug-disease precaution early detection and refinement. The outcome of the Systematized Nomenclature of Medicine (SNOMED) concept learning system will help mitigate the impact of Adverse Drug Events hence directly contribute to the National Research Priority promoting and maintaining good health. It will tailor SNOMED knowledge to different clinical settings and provide evidence-based preventative health care. Th ....Practice-based Systematized Nomenclature of Medicine (SNOMED) concept learning for drug-disease precaution early detection and refinement. The outcome of the Systematized Nomenclature of Medicine (SNOMED) concept learning system will help mitigate the impact of Adverse Drug Events hence directly contribute to the National Research Priority promoting and maintaining good health. It will tailor SNOMED knowledge to different clinical settings and provide evidence-based preventative health care. The enabling methodology from this project for building computerised cognitive learning systems will be a frontier technology to enhance smart information use in clinical decision support. It will also contribute to the development of knowledge-based systems. A network version of the developed system will assist doctors working in rural and remote areas with their clinical decision making and prescribing practice.Read moreRead less
Supporting Responses To Commonwealth Science Council Priorities - Grant ID: CS170100008
Funder
Australian Research Council
Funding Amount
$209,346.00
Summary
Deployment of artificial intelligence and what it presents for Australia. This project aims to explore the opportunities, risks and consequences of broad uptake of artificial intelligence (AI) and to collate evidence on the economics, social perspectives, research capabilities and environmental impacts. As AI becomes more advanced, its applications will become increasingly complex in applications in homes, workplaces and cities. Taking an interdisciplinary approach to explore the opportunities, ....Deployment of artificial intelligence and what it presents for Australia. This project aims to explore the opportunities, risks and consequences of broad uptake of artificial intelligence (AI) and to collate evidence on the economics, social perspectives, research capabilities and environmental impacts. As AI becomes more advanced, its applications will become increasingly complex in applications in homes, workplaces and cities. Taking an interdisciplinary approach to explore the opportunities, risks and benefits of AI, this project will examine the economic, social, ethical and cultural aspects of deployment and will present a set of key findings to guide and support policy making over the next decade.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
Discovery Early Career Researcher Award - Grant ID: DE140101749
Funder
Australian Research Council
Funding Amount
$379,480.00
Summary
A computational network model of the mental lexicon. Understanding a word's meaning is a challenge when learning a language and a capacity that is seriously affected in various disorders such as Alzheimer's disease, however little is known about how meaning is organised in the mental lexicon and evolves from childhood into old age. This project aims to build a detailed computational model integrating information available through the senses and structure in the language environment to derive a l ....A computational network model of the mental lexicon. Understanding a word's meaning is a challenge when learning a language and a capacity that is seriously affected in various disorders such as Alzheimer's disease, however little is known about how meaning is organised in the mental lexicon and evolves from childhood into old age. This project aims to build a detailed computational model integrating information available through the senses and structure in the language environment to derive a lexicon that covers most words people know. By distinguishing qualitative different types of meaning relations, this project will allow the prediction of the kind of information and processes required to understand words and an understanding of how this lexicon grows in childhood and declines in old age.Read moreRead less