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

Bauer, Matthias

doi:10.17863/CAM.50344

Item

ITEM ACTIONSEXPORT

Add to Basket

Local TagsRelease HistoryDetailsSummary

Released

Thesis

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

MPS-Authors

/persons/resource/persons220009

Bauer, Matthias
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

External Resource

No external resources are shared

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

Fulltext (public)

There are no public fulltexts stored in PuRe

Supplementary Material (public)

There is no public supplementary material available

Citation

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

Cite as: https://hdl.handle.net/21.11116/0000-0007-1E6D-F

Abstract

There is no abstract available