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Field
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Adversarial Machine Learning (AML) is a technique to fool a machine learning model through malicious input. Due to its significance in many scenarios, including security, privacy, and health
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information about behavioural patterns, but scoring this manually is time consuming. For this reason, machine learning solutions have been developed to automate behavioural prediction [5-12]. DeepLabCut [5] is
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The world is dynamic and in a constant state of flux, yet most machine learning models learn static models from a dataset that represents a single snapshot in time. My group's research is
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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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-disciplinary team of clinician scientists and computer scientists to develop diagnosis/predictive/treatment/robotics surgery models of diseases of interest using multimodal medical data, consisting of images
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You Completion (level A and B) or near completion (level A) of a PhD in the field of Information Retrieval, Natural Language Processing, or Machine Learning on Textual Data. Demonstrated expert
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Project description: Nowadays, data-driven machine learning algorithms are well suited to solve real-world problems that require high-level prediction accuracy. However, it seems as if nothing beats
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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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With success stories ranging from speech recognition to self-driving cars, machine learning (ML) has been one of the most impactful areas of computer science. ML’s versatility stems from the wealth
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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