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Neuro-symbolic AI combines the strengths of neural and symbolic methods to efficiently learn and reason over models of the world. Typically, many of the assurances that can be provided by
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Neuro-symbolic AI combines the strengths of neural and symbolic methods to efficiently learn and reason over models of the world. Typically, many of the assurances that can be provided by
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operators for these notions. Over the past fifty years, such non-classical logics have proved vital in computer science and logic-based artificial intelligence: after all, any intelligent agent must be able
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This project investigates 3Vs of Big Data (e.g Volume, Variety, and Velocity). Volume: Due to the exponential increase in data volume, it is necessary to adopt parallelism techniques to achieve
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capital markets, financial modelling and valuations Exceptional management and leadership experience Highly developed numerical, conceptual and analytical skills, and technical expertise to resolve complex
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DNA or RNA motif discovery is a popular biological method to identify over-represented DNA or RNA sequences in next generation sequencing experiments. These motifs represent the binding site
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Dowe, 1999a) ensures that - at least in principle, given enough search time - MML can infer any underlying computable model in a data-set. A consequence of this is that we can (e.g.) put latent factor
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anomalies in evolving graphs. In this research proposal, our aim is to explore the parallels of deep learning and anomaly detection in dynamic graphs. In particular we are interested to redesign deep neural
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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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Project Description Recent advances in mixed reality (MR) technology, which seamlessly blend the physical environment with computer-generated content around the user, have reduced the barriers