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 4 of 4 for “"Deep Gaussian Process"”.
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Deep Gaussian Process Surrogates for Computer Experiments
Deep Gaussian processes (DGPs) upgrade ordinary GPs through functional composition, in which intermediate GP layers warp the original inputs, providing flexibility to model non-stationary dynamics. Recent applications in machine learning favor approximate, optimization-based inference for fast …
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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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Physics-informed Machine Learning for Digital Twins of Metal Additive Manufacturing
… and includes monitoring and controlling the process in a real-world manufacturing environment. Digital Twins (DTs) are virtual representations of physical systems that enable fast and accurate decision-making. DTs rely on Artificial Intelligence (AI) to process complex information from …
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Contributions to asymptotic theory in nonparametric statistics
… Taking a Bayesian approach, we consider a deep Gaussian process (DGP) prior, which is designed to leverage the compositional structure of the parameter in order to achieve fast convergence rates. We show that the DGP prior does indeed consistently solve the inverse problem, proving a …