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Citizenry Street-Level Environment Recognition On Moving Resource-Constrained Devices Bayesian Generative AI (PhD Project) Explainability and Compact representation of K-MDPs Creating a 21st Century Helpline
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Processing/Control Path Planning/Trajectory Planning Multi-Target Tracking/Multi-Object Tracking, Bayesian Filtering, Radom Finite Set filters or closely related multi-target tracking approaches in radar
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the Faculty of Science. We will apply Bayesian approaches such as the information-theoretic minimum message length (MML) principle and other approaches to develop a path towards statistically-optimal algorithms
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for the prediction task, such as predicting the bioassay of a given chemical network. One of the approaches that will be considered will be the Bayesian information-theoretic Minimum Message Length (MML) principle
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back at least as far as 1954 (Dowe, 2008a, sec. 1, pp549-550). Discussion of how to do this using the Bayesian information-theoretic minimum message length (MML) approach (Wallace and Boulton, 1968
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Methods of balancing model complexity with goodness of fit include Akaike's information criterion (AIC), Schwarz's Bayesian information criterion (BIC), minimum description length (MDL) and minimum
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. Among the approaches used will be the Bayesian information-theoretic Minimum Message Length (MML) principle (Wallace and Boulton, 1968; Wallace and Dowe, 1999a; Wallace, 2005) References: Wallace, C.S
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of a GIS-Based Model for Active Citizenry Street-Level Environment Recognition On Moving Resource-Constrained Devices Bayesian Generative AI (PhD Project) Explainability and Compact representation of K
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used will the information-theoretic Bayesian minimum message length (MML) principle. Student cohort PhD, possibly Master’s (Minor Thesis) or Honours URLs/references Chen, Li and Gao, Jiti and Vahid
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techniques for annotation, active learning (based on either deep learning or Bayesian learning), semi-supervised learning, transfer learning, imitation learning, etc., aiming to ensure the data and models