# Latent variable model

## Papers overview

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Highly Cited

2017

Highly Cited

2017

- ICLR
- 2017

Natural image modeling is a landmark challenge of unsupervised learning. Variational Autoencoders (VAEs) learn a useful latentâ€¦Â (More)

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Highly Cited

2015

Highly Cited

2015

- NIPS
- 2015

In this paper, we explore the inclusion of latent random variables into the dynamic hidden state of a recurrent neural networkâ€¦Â (More)

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Highly Cited

2010

Highly Cited

2010

- AISTATS
- 2010

We introduce a variational inference framework for training the Gaussian process latent variable model and thus performingâ€¦Â (More)

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Highly Cited

2010

Highly Cited

2010

- EMNLP
- 2010

The rapid growth of geotagged social media raises new computational possibilities for investigating geographic linguisticâ€¦Â (More)

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Highly Cited

2008

Highly Cited

2008

- ICML
- 2008

In dimensionality reduction approaches, the data are typically embedded in a Euclidean latent space. However for some data setsâ€¦Â (More)

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Highly Cited

2007

Highly Cited

2007

- ICML
- 2007

Supervised learning is difficult with high dimensional input spaces and very small training sets, but accurate classification mayâ€¦Â (More)

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Highly Cited

2007

Highly Cited

2007

- IJCAI
- 2007

WiFi localization, the task of determining the physical location of a mobile device from wireless signal strengths, has beenâ€¦Â (More)

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Highly Cited

2007

Highly Cited

2007

- ICML
- 2007

The Gaussian process latent variable model (GP-LVM) is a powerful approach for probabilistic modelling of high dimensional dataâ€¦Â (More)

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Highly Cited

2003

Highly Cited

2003

- NIPS
- 2003

In this paper we introduce a new underlying probabilistic model for principal component analysis (PCA). Our formulationâ€¦Â (More)

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Highly Cited

1998

Highly Cited

1998

- IEEE Trans. Pattern Anal. Mach. Intell.
- 1998

Visualization has proven to be a powerful and widely-applicable tool for the analysis and interpretation of multivariate dataâ€¦Â (More)

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