no code implementations • 13 Feb 2024 • Dan MacKinlay, Russell Tsuchida, Dan Pagendam, Petra Kuhnert
The use of local messages in a graphical model structure ensures that the approach is suited to distributed computing and can efficiently handle complex dependence structures.
no code implementations • 16 Jan 2024 • Conrad Sanderson, Emma Schleiger, David Douglas, Petra Kuhnert, Qinghua Lu
While the operationalisation of high-level AI ethics principles into practical AI/ML systems has made progress, there is still a theory-practice gap in managing tensions between the underlying AI ethics aspects.
no code implementations • 10 May 2023 • Andrew Bolt, Conrad Sanderson, Joel Janek Dabrowski, Carolyn Huston, Petra Kuhnert
When compared to a related neural model (emulator) which was employed to generate probability maps via ensembles of emulated fires, the proposed approach produces competitive Jaccard similarity scores while being approximately an order of magnitude faster.
1 code implementation • 2 Dec 2022 • Joel Janek Dabrowski, Daniel Edward Pagendam, James Hilton, Conrad Sanderson, Daniel MacKinlay, Carolyn Huston, Andrew Bolt, Petra Kuhnert
We show that popular optimisation cost functions used in the literature can result in PINNs that fail to maintain temporal continuity in modelled fire-fronts when there are extreme changes in exogenous forcing variables such as wind direction.
no code implementations • 17 Jun 2022 • Andrew Bolt, Carolyn Huston, Petra Kuhnert, Joel Janek Dabrowski, James Hilton, Conrad Sanderson
We propose a dedicated spatio-temporal neural network based framework for model emulation, able to capture the complex behaviour of fire spread models.
no code implementations • 23 Mar 2022 • Andrew Bolt, Joel Janek Dabrowski, Carolyn Huston, Petra Kuhnert
Empirical observations of bushfire spread can be used to estimate fire response under certain conditions.