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Primary supervisor Levin Kuhlmann Research area Machine Learning This project involves model-based depth of anaesthesia monitoring using autoregressive moving average modelling and neural mass and
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mental models of the actors involved in AI systems, designers, agents and users and expand on the "Model Reconciliation Problem" or reconciling the different actors' beliefs and intentions. We will
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Primary supervisor Levin Kuhlmann Research area Machine Learning This project involves model-based depth of anaesthesia monitoring using autoregressive moving average modelling and neural mass and
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mental models of the actors involved in AI systems, designers, agents and users and expand on the "Model Reconciliation Problem" or reconciling the different actors' beliefs and intentions. We will
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music and movement have always been intrinsically linked, the rise of the laptop DJ broke that nexus. At worst, stagecraft has recently been reduced to little more than email checking performances. The
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music and movement have always been intrinsically linked, the rise of the laptop DJ broke that nexus. At worst, stagecraft has recently been reduced to little more than email checking performances. The
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to enable distributed clients (e.g., mobile devices) to jointly train a machine learning model without pooling their raw data into a centralised server. Because data never leaves from user clients
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publications, citations or scientific careers to test the implications of a model. References Fortunato, S. et al. “Science of science.” Science 359 (2018): eaao0185. https://doi.org/10.1126
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publications, citations or scientific careers to test the implications of a model. References Fortunato, S. et al. “Science of science.” Science 359 (2018): eaao0185. https://doi.org/10.1126
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accuracy is not high when we apply them on brain EEG time series (65-70%). This is because there is a large variation between EEG data of different subjects, so a TSC model cannot generalise on unseen