Nested Variational Compression in Deep Gaussian Processes

James Hensman; Neil D. Lawrence

edit

Back to publications

Nested Variational Compression in Deep Gaussian Processes

James Hensman, Neil D. Lawrence

, 2014.

Abstract

Deep Gaussian processes provide a flexible approach to probabilistic modelling of data using either supervised or unsupervised learning. For tractable inference approximations to the marginal likelihood of the model must be made. The original approach to approximate inference in these models used variational compression to allow for approximate variational marginalization of the hidden variables leading to a lower bound on the marginal likelihood of the model [Damianou and Lawrence, 2013]. In this paper we extend this idea with a nested variational compression. The resulting lower bound on the likelihood can be easily parallelized or adapted for stochastic variational inference.

Links

Cite this Paper

BibTeX


@Misc{Hensman-nested14,
  title = 	 {Nested Variational Compression in Deep {G}aussian Processes},
  author = 	 {Hensman, James and Lawrence, Neil D.},
  year = 	 {2014},
  pdf = 	 {https://arxiv.org/pdf/1412.1370.pdf},
  url = 	 {/publications/hensman-nested14.html},
  abstract = 	 {Deep Gaussian processes provide a flexible approach to probabilistic modelling of data using either supervised or unsupervised learning. For tractable 
inference approximations to the marginal likelihood of the model must be made. The original approach to approximate inference in these models used 
variational compression to allow for approximate variational marginalization of the hidden variables leading to a lower bound on the marginal likelihood 
of the model [Damianou and Lawrence, 2013]. In this paper we extend this idea with a nested variational compression. The resulting lower bound on the 
likelihood can be easily parallelized or adapted for stochastic variational inference.
}
}

Endnote

%0 Generic
%T Nested Variational Compression in Deep Gaussian Processes
%A James Hensman
%A Neil D. Lawrence
%D 2014	
%F Hensman-nested14
%U /publications/hensman-nested14.html
%X Deep Gaussian processes provide a flexible approach to probabilistic modelling of data using either supervised or unsupervised learning. For tractable 
inference approximations to the marginal likelihood of the model must be made. The original approach to approximate inference in these models used 
variational compression to allow for approximate variational marginalization of the hidden variables leading to a lower bound on the marginal likelihood 
of the model [Damianou and Lawrence, 2013]. In this paper we extend this idea with a nested variational compression. The resulting lower bound on the 
likelihood can be easily parallelized or adapted for stochastic variational inference.

RIS


TY  - GEN
TI  - Nested Variational Compression in Deep Gaussian Processes
AU  - James Hensman
AU  - Neil D. Lawrence
DA  - 2014/01/01	
ID  - Hensman-nested14
L1  - https://arxiv.org/pdf/1412.1370.pdf
UR  - /publications/hensman-nested14.html
AB  - Deep Gaussian processes provide a flexible approach to probabilistic modelling of data using either supervised or unsupervised learning. For tractable 
inference approximations to the marginal likelihood of the model must be made. The original approach to approximate inference in these models used 
variational compression to allow for approximate variational marginalization of the hidden variables leading to a lower bound on the marginal likelihood 
of the model [Damianou and Lawrence, 2013]. In this paper we extend this idea with a nested variational compression. The resulting lower bound on the 
likelihood can be easily parallelized or adapted for stochastic variational inference.

ER  -

APA


Hensman, J. & Lawrence, N.D.. (2014). Nested Variational Compression in Deep Gaussian Processes.  Available from /publications/hensman-nested14.html.