Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 6 of 6 for “"Evidential Deep Learning"”.
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Evidential Deep Learning for uncertainty quantification in jet tagging deep neural network model
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-05-01
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Learning-Based Complex Terrain Navigation Under Uncertainty
… both aleatoric and epistemic uncertainty by learning discrete traversability distributions and probability densities of the traversability predictor’s latent features. Leveraging evidential deep learning, this work parameterizes Dirichlet distributions with network outputs and proposes a …
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Machine Learning Methods for Discovering Metabolite Structures from Mass Spectra
… integrate chemistry-informed priors with modern deep learning advancements. I begin by decomposing and framing the metabolite annotation pipeline into four key tasks well-fit for supervised deep learning including (A) molecular formula prediction, (B) spectrum-to-molecule property prediction, (C) …
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Speech-Based Emotion Modelling and Mental Disorder Detection
… as samples drawn from the emotion distribution. Evidential deep learning (EDL) is used to quantify the uncertainty in emotion distribution estimation by learning an utterance-specific prior distribution. Representing emotion as a distribution offers not only a more comprehensive representation of …
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Reliable and decentralised deep learning for physiological data
… functions and processes. By employing machine learning to model these data, especially with the advancement of mobile sensing technologies, it becomes feasible to automatically and continually monitor and diagnose one's health status. This holds considerable promise for easing the burden on …
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Uncertainty-aware learning from sparse, unlabelled, and out-of-distribution time series
Nowadays, machine learning is increasingly popular in the analysis of healthcare time series, as it can support improved diagnostics, personalised monitoring, and effective performance tracking. The ever-increasing availability of datasets from wearable sensors, mobile devices, and continuous …