Analysing Iterative Machine Learning Algorithms with Information Geometric Methods. Online machine learning problems arise from situations where data is provided a point at a time. There are many classical algorithms for solving such problems based on the principle of stochastic gradient descent. Recent research by the CIs and others have thrown up interesting but diverse geometric connections that offer new insights. The proposed research aims to integrate the understanding of these algori ....Analysing Iterative Machine Learning Algorithms with Information Geometric Methods. Online machine learning problems arise from situations where data is provided a point at a time. There are many classical algorithms for solving such problems based on the principle of stochastic gradient descent. Recent research by the CIs and others have thrown up interesting but diverse geometric connections that offer new insights. The proposed research aims to integrate the understanding of these algorithms with the aim of designing algorithms better able to exploit prior knowledge, and to extend existing algorithms to new problem domains thus offering well principled and well understood algorithms for solving a variety of novel online problems.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE170100128
Funder
Australian Research Council
Funding Amount
$395,000.00
Summary
Information processing in the brain. This project aims to understand the brain's functional organisation by developing non-invasive methods to characterise connectivity between interacting brain regions. No model-based methods to compute directional coupling between brain regions can be applied to large scale networks for resting state functional MRI data. This capability would be a major breakthrough in neuroimaging, given uninformative (non-directional) network connectivity analysis restricts ....Information processing in the brain. This project aims to understand the brain's functional organisation by developing non-invasive methods to characterise connectivity between interacting brain regions. No model-based methods to compute directional coupling between brain regions can be applied to large scale networks for resting state functional MRI data. This capability would be a major breakthrough in neuroimaging, given uninformative (non-directional) network connectivity analysis restricts research. This project is expected to advance our understanding of information processing in the brain by providing a mechanistic approach to functional integration.Read moreRead less