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 15 of 15 for “"bayesian deep learning"”.
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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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UNCERTAINTY QUANTIFICATION AND DECOMPOSITION THROUGH BAYESIAN DEEP LEARNING FOR BIG DATA SATELLITE REMOTE SENSING PROBLEMS
… in both classification and regression settings. Bayesian deep learning methods are applied to a classification and a regression problem with datasets in excess of 14 million samples, quantifying total uncertainty and decomposing the total uncertainty into separate components. In all cases, …
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Towards Better Representations with Deep/Bayesian Learning
<p>Deep learning and Bayesian Learning are two popular research topics in machine learning. They provide the flexible representations in the complementary manner. Therefore, it is desirable to take the best from both fields. This thesis focuses on the intersection of the two topics— enriching one …
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Approximate Inference: New Visions
Nowadays machine learning (especially deep learning) techniques are being incorporated to many intelligent systems affecting the quality of human life. The ultimate purpose of these systems is to perform automated decision making, and in order to achieve this, predictive systems need to return …
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Mathematical analysis of uncertainty in machine learning and deep learning
In this paper, we study uncertainty in machine learning and deep learning from the mathematical point of view. Uncertainty is involved in many real-world situations. The Bayesian modelling can handle such uncertainty in machine learning community. However, the traditional deep learning model fails …
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Robot Motions that Mitigate Uncertainty
… on robot motion decisions in the context of deep learning-based perception uncertainty. The first part of this dissertation introduces a risk-aware framework for path planning and assignment of multiple robots and multiple demands in unknown environments. The second part introduces a …
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Geometry and Uncertainty in Deep Learning for Computer Vision
Deep learning and convolutional neural networks have become the dominant tool for computer vision. These techniques excel at learning complicated representations from data using supervised learning. In particular, image recognition models now out-perform human baselines under constrained settings. …
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Designing Highly-Efficient Hardware Accelerators for Robust and Automatic Deep Learning Technologies
Deep learning based 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 …
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Advances in approximate inference: combining VI and MCMC and improving on Stein discrepancy
In the modern world, machine learning, including deep learning, has become an indispensable part of many intelligent systems, helping people automate the decision-making process. For certain applications (e.g. health care services), reliable predictions and trustworthy decisions are crucial factors …
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Towards Improved Variational Inference for Deep Bayesian Models
Deep learning has revolutionized the last decade, being at the forefront of extraordinary advances in a wide range of tasks including computer vision, natural language processing, and reinforcement learning, to name but a few. However, it is well-known that deep models trained via maximum …
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Statistical models and decision making for robotic scientific information gathering
… gathering, demonstrating how theory from machine learning, decision theory, theory of optimal experimental design, and statistical inference can be used to develop online algorithms for robotic information gathering that are robust to modeling errors, account for spatiotemporal structure in …
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Statistical models and decision making for robotic scientific information gathering
… gathering, demonstrating how theory from machine learning, decision theory, theory of optimal experimental design, and statistical inference can be used to develop online algorithms for robotic information gathering that are robust to modeling errors, account for spatiotemporal structure in …
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Efficient, robust and uncertainty aware mobile health
… illness. This data can be fed into machine learning algorithms aiming to help practitioners better assess the progress of the patient or other aspects of the disease evolution. Therefore, it is essential to have accurate model predictions and, equally significantly, a better understanding of …
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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
<p>Deep learning models, which form the backbone of modern ML systems, generalize poorly to small changes to the data distribution. They are also bad at signalling failure, making predictions with high confidence when their training data or fragile assumptions make them unlikely to make reasonable …