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units and w's represent the weights of the neural network. References: [1] Buser Say, Ga Wu, Yu Qing Zhou and Scott Sanner. Nonlinear hybrid planning with deep net learned transition models and mixed
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levels for manufacturing, routing delivery trucks for transport, scheduling power stations and electricity grids, to name just a few. In recent years, deep learning is showing startling ability
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the shortcomings of these techniques, deep learning is more and more involved in static vulnerability localization and improving fuzzing efficiency. This project aims to deliver a smart software vulnerability
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‘dynamic graphs’. Although recently many studies on extending deep learning approaches for graph data have emerged, there is still a research gap on extending deep learning approaches for identifying
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Project description: On behalf of the Victorian Orthopaedic Trauma Outcomes Registry (VOTOR), we will establish the role of artificial intelligence (AI) deep learning to improve the prediction
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segmentation and classification; for example, segmenting tumour from the medical images, and then classify the grade of the tumour. We will use various Deep Learning techniques, such as CNN, and will experiment
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Discovery Project, this research aims to develop highly novel physics-informed deep learning methods for Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) and applications in image
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for intelligent systems to guard their execution and evolution. Required knowledge - deep learning - software development lifecycle
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In recent years, AI techniques such as GANs and associated deep learning neural networks have become popular tools applied to the production and creation of works of art. In 2018, AI Art made
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techniques. It may also include hardware development of wearable assistive devices that use audio and haptic feedback. Required knowledge Image processing Computer vision Deep learning Programming (Python, C