Sparse Inducing Points in Deep Gaussian Processes: Enhancing Modeling with Denoising Diffusion Varia...
Sparse Inducing Points in Deep Gaussian Processes: Enhancing Modeling with Denoising Diffusion Variational Inference
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Author / Creator
Xu, Jian , Zeng, Delu and Paisley, John
Publisher
Ithaca: Cornell University Library, arXiv.org
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English
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Ithaca: Cornell University Library, arXiv.org
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Contents
Deep Gaussian processes (DGPs) provide a robust paradigm for Bayesian deep learning. In DGPs, a set of sparse integration locations called inducing points are selected to approximate the posterior distribution of the model. This is done to reduce computational complexity and improve model efficiency. However, inferring the posterior distribution of...
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Full title
Sparse Inducing Points in Deep Gaussian Processes: Enhancing Modeling with Denoising Diffusion Variational Inference
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TN_cdi_proquest_journals_3084544771
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_3084544771
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E-ISSN
2331-8422
