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understood. This project works at the intersection of dynamical modelling, statistical signal processing, statistical inference and machine learning to develop subject specific mathematical models of
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understood. This project works at the intersection of dynamical modelling, statistical signal processing, statistical inference and machine learning to develop subject specific mathematical models of
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, scalability, and solution quality for tasks such as: Similarity search and nearest-neighbour queries Graph and routing optimisation Large-scale data processing Intelligent and educational analytics
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; Haffari, Reza; Qi, Guilin. Few-shot Complex Knowledge Bases Question Answering via Meta Reinforcement Learning. Conference on Empirical Methods in Natural Language Processing (EMNLP'20) . Hua, Yuncheng
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range of topic and contextual domains. Moreover, such language processing must deal with slang, foreign words and passages, localised dialects, unpredictably constructed language, and linguistic
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; Haffari, Reza; Qi, Guilin. Few-shot Complex Knowledge Bases Question Answering via Meta Reinforcement Learning. Conference on Empirical Methods in Natural Language Processing (EMNLP'20) . Hua, Yuncheng
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range of topic and contextual domains. Moreover, such language processing must deal with slang, foreign words and passages, localised dialects, unpredictably constructed language, and linguistic
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Scheduling : Example: For non-urgent data processing, SmartScaleSys (S3) could prioritize tasks during off-peak hours when renewable energy sources are more available, reducing carbon emissions
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for Markov Decision Processes. The application areas are different problems in text processing, including (bu not limited to) machine translation and summarisation. Our previous work in this area
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for Markov Decision Processes. The application areas are different problems in text processing, including (bu not limited to) machine translation and summarisation. Our previous work in this area