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cooperating with each other, but in many cases competing for individual gains. This structure may not always work for the benefit of science. The purpose of this project is to use game theory and computational
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. This work combines computational modelling and simulation with biological experiments that are analysed using cutting-edge computer vision techniques. We collaborate closely with Macquarie University where
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methods dealing with model complexity - e.g., AIC, BIC, MDL, MML - can enhance deep learning. References: D. L. Dowe (2008a), "Foreword re C. S. Wallace ", Computer Journal , Vol. 51, No. 5 (Sept. 2008
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models that can forecast the likely outcomes of current practices. The project aims to develop cutting-edge machine learning and statistical risk prediction techniques to predict each short-term, long-term
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are made where and when; supernovae (mechanisms and nucleosynthesis); gamma-ray bursts and their progenitors; modelling of Type I X-ray bursts and superbursts (thermonuclear explosions on the surface
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information in the spatial context of the task at hand. To achieve this the computer guidance system needs to be aware of the environment through a rich digital-twin model that is kept up-to-date in the face
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of the Association for Computational Linguistics - Human Language Technologies (NAACL-HLT), 2019. [2] Contextual Neural Model for Translating Bilingual Multi-Speaker Conversations Sameen Maruf, Andre Martins
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of Decision Trees, Support Vector Machines and Logistic Regression Models based on Pre-Computation", IEEE Transactions on Dependable and Secure Computing 2019 #digitalhealth
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testing approaches that can be used to verify that machine learning models are not biased. Required knowledge Software engineering, software testing, statistics, machine learning
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system to unlock important information from unstructured data sources including X-ray images, surgical and radiology text reports. We will compare prediction models based on existing, routinely collected