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-view learning that are robust under distribution shift and missing modalities. The key objectives include: Develop scalable Bayesian deep learning methods for uncertainty estimation in modern neural
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analysis, contextual analysis, audio feature extraction, and machine learning models to identify and assess potentially dangerous content. Similarly, computer vision models are implemented to analyse images
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methods dealing with model complexity - e.g., AIC, BIC, MDL, MML - can enhance deep learning. References: D. L. Dowe (2008a), "Foreword re C. S. Wallace", Computer Journal, Vol. 51, No. 5 (Sept. 2008
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tissues or reveal micro- or nano-structural features, like the small air sacs in lungs. To overcome these limitations, alternative X-ray imaging methods have been developed: X-ray phase-contrast and dark
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privacy constraints, robust solutions are essential. This PhD project will develop methods for building reliable medical imaging models that generalize across distribution shifts without retraining
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to Fisher-Rao geometry and develop theoretical results characterising MML estimation under various regularity conditions. Aim 2: Development of Computational Methods for MML Design and implement efficient
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that explicitly account for drift and open-set contamination. O2: Robust uncertainty estimation: Improve calibration and uncertainty reliability under drift (e.g., ensembles, Bayesian approximations, conformal
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-Kolmogorov complexity (Wallace and Dowe, 1999a), it is possible to (e.g.) create a general hybrid of (none, some or) all of the above methods - and then (if we wish) to generalise that even further
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technologies will affect them. It is our anticipation that the work will commence with, in parallel, the survey for collecting the data and a comparison of machine learning methods on artificial pseudo-randomly
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time. In this project, we propose a method for identifying and classifying such emerging asynchronous trends. The goal is to be able to predict how a new emerging trend will develop using similar