Advances in Probabilistic Modelling: Sparse Gaussian Processes, Autoencoders, 
and Few-shot Learning

Bauer, Matthias

doi:10.17863/CAM.50344

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Advances in Probabilistic Modelling: Sparse Gaussian Processes, Autoencoders, and Few-shot Learning

Bauer, M. (2019). Advances in Probabilistic Modelling: Sparse Gaussian Processes, Autoencoders, and Few-shot Learning. PhD Thesis, University of Cambridge, Cambridge.

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Item Permalink: https://hdl.handle.net/21.11116/0000-0007-1E6D-F Version Permalink: https://hdl.handle.net/21.11116/0000-0009-CADB-D

Genre: Thesis

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Creators:
Bauer, Matthias¹, Author

Affiliations:
1Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497647

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Free keywords: Abt. Schölkopf

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Language(s): eng - English

Dates: Accepted: 2019-11-28

Publication Status: Accepted / In Press

Pages: XII, 240

Publishing info: Cambridge : University of Cambridge

Table of Contents: -

Rev. Type: -

Identifiers: DOI: 10.17863/CAM.50344

Degree: PhD

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