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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 20 of 23 for “"Bayesian neural networks"”.
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FEDERATED LEARNING OF BAYESIAN NEURAL NETWORKS
Although federated learning and Bayesian neural networks have been researched, there are few implementations of the federated learning of Bayesian networks. In this thesis, a federated learning training environment for Bayesian neural networks using a public code base, Flower, is developed. With it …
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Bayesian Neural Networks for Actuarial Mortality Modelling
The use of Bayesian neural networks (BNNs) for mortality modelling is an understudied, yet potentially promising area of research. They inherently offer robust uncertainty quantification, and are known for their application to sparse or small datasets. This research investigates the efficacy of BNN …
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Efficient Ensemble-based Bayesian Neural Networks for Depth Regression
… In many application areas, the decisions of Neural Networks (NNs) are safety-critical and incorrect ones can lead to serious consequences. However, traditional NNs do not provide any indication about the certainty of their predictions, which can lead to overconfidence, especially in …
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Multi-Level Quantization of Stochastic Variational Inference based Bayesian Neural Networks
Bayesian Neural Networks (BNNs) integrate the representational power of standard neural networks with the uncertainty estimation capabilities of Bayesian Inference, offering a robust framework to address challenges such as overconfidence and overfitting. However, the inherent complexity of BNNs …
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Studies on the generalisation of Gaussian processes and Bayesian neural networks
… Over the recent years Gaussian processes and Bayesian neural networks have come to the fore and in this thesis their generalisation capabilities are analysed from theoretical and empirical perspectives. Upper and lower bounds on the learning curve of Gaussian processes are investigated in …
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Deep models, lighter footprint compressing, explaining, and transferring Bayesian neural networks
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms
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Approximate Inference in Bayesian Neural Networks and Translation Equivariant Neural Processes
… two such probabilistic machine learning models: Bayesian neural networks and neural processes. Bayesian neural networks are a classical model that has been the subject of research since the 1990s. They rely on Bayesian inference to represent uncertainty in the weights of a neural network. On the …
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Harnessing the power of machine learning, Bayesian neural networks, and spatial analysis in modeling a predictive system, credit risk, and organizational performance across continents
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-02-04 without embargo terms
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Towards Improved Variational Inference for Deep Bayesian Models
… and give poorly-calibrated predictions. Bayesian deep learning attempts to address this by placing priors on the model parameters, which are then combined with a likelihood to perform posterior inference. Unfortunately, for deep models, the true posterior is intractable, forcing the user …
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Function-Space Bayesian Learning: from Gaussian Processes to Bayesian Deep Learning
Bayesian methods provide a general and principled framework to quantify and update beliefs based on prior knowledge and observed evidence. This thesis presents my works about function-space Bayesian learning, whose priors and posteriors are specified and computed over the function-space. This …
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A methodology for the characterization of business-to-consumer E-commerce.
… of marginalisation to the empirical prior, for Bayesian neural networks, to deal with the use of class-unbalanced data sets; a study of the Generative Topographic Mapping (GTM) as a principled method for market segmentation, including some developments of the model, namely: a) an entropy-based …
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Finite Gaussian Neurons: Defending Against Adversarial Attacks by Making Neural Networks Say "I Don’t Know"
… a novel neuron architecture for artificial neural networks aimed at protecting against adversarial attacks.<br />Since 2014, artificial neural networks have been known to be vulnerable to adversarial attacks, which can fool the network into producing wrong or nonsensical outputs by making …
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Alloy Design for a Fusion Power Plant
… material. To aid understanding of these effects, Bayesian neural networks were used to model irradiation hardening and embrittlement of a set of candidate alloys, reduced-activation ferritic-martensitic steels. The models have been compared to other methods, and it is demonstrated that a neural …
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Novel Approaches to Discovery and Optimization in Physics: Symbolic Regression, Bayesian Optimization, and Topological Photonics
… and inverse design. First, I propose a neural network-based method to perform symbolic regression and automatically learn the underlying equations from high-dimensional and complex datasets. The neural network-based model can integrate with other deep learning architectures, thus taking …
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Probabilistic machine learning for the elimination of thermoacoustic instabilities
… The current thesis demonstrates how Bayesian machine learning techniques may be of benefit when modeling, designing against and trying to avoid thermoacoustic instabilities. We show that Bayesian Neural Network can be used to assimilate model parameters from flame data and make our …
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Accurate Uncertainty Quantification and Explainable Artificial Intelligence in Machine Learning Models for Toxicological Risk Assessment
… three different modelling methodologies (Bayesian bootstrapping, conformal prediction, and Bayesian neural networks) on a diverse dataset of 21 toxicologically relevant targets identified by Allen et al. (2022). Metrics to evaluate uncertainty quantification are defined and four …
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Designing Highly-Efficient Hardware Accelerators for Robust and Automatic Deep Learning Technologies
… AI technologies, such as deep convolutional neural networks (DNNs), have recently achieved amazing success in numerous applications, such as image recognition, autonomous driving, and so on. However, there are two critical issues in the conventional DNN applications. The first problem is …
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Bayesian Learning for Data-Efficient Control
… especially small datasets. We use probabilistic Bayesian modelling to learn systems from scratch, similar to the PILCO algorithm, which achieved unprecedented data efficiency in learning control of several benchmarks. We extend PILCO in three principle ways. First, we learn control under …
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Improved Sampling and Variational Inference Methods for Neural Networks
Bayesian Neural Networks (BNNs), an application of Bayesian inference to neural networks, offer an alternative way of training. They combine multiple weight settings, each compatible with the training data, and quantify the uncertainty about the network's weights. While Bayesian inference applied …
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Multimodal Probabilistic Inference for Robust Uncertainty Quantification
… probabilistic modelling approaches such as Bayesian neural networks (BNN) do not scale well in terms of memory and runtime and often underperform simple deterministic baselines in terms of accuracy. Furthermore, BNNs underperform deep ensembles as they fail to explore multiple modes, in the …
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