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., Pan, S., Aggarwal, C., & Salehi, M. (2022). Deep learning for time series anomaly detection: A survey. ACM Computing Surveys.
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with the selected vendor, SAP Payroll and Integration Teams, all of which are expected to be fully operationalised by May 2026. This role requires deep expertise in enterprise workforce management
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in diverse, real-world environments. Both classical machine learning methods and deep learning techniques can be employed to tackle this task. This project aims to achieve several objectives: 1
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This Ph.D. project aims to combine causal analysis with deep learning for mental health support. As deep learning is vulnerable to spurious correlations, novel causal discovery and inference methods
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Finance Team, ensuring compliance with legislative requirements and financial governance policies. You'll provide invaluable support in developing business proposals and managing risks, while gaining a deep
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experience in at least one of the following areas: deep learning, active learning, deep reinforcement learning, and natural language processing.
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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