article · 2007

Efficient bounds for the softmax function, applications to inference in hybrid models

Guillaume Bouchard

Cited by 0Abstract and details only

Abstract

The softmax link is used in many probabilistic model dealing with both discrete and continuous data. However, efficient Bayesian inference for this type of model is still an open problem due to the lack of efficient upper bound for the sum of exponentials. We propose three different bounds for this function and study their approximation properties. We give a direct application to the Bayesian treatment of multiclass logistic regression and discuss its generalization to deterministic approximate inference in hybrid probabilistic graphical models.

Cite this paper

APA 7

Bouchard, G. (2007). Efficient bounds for the softmax function, applications to inference in hybrid models.

MLA 9

Bouchard, Guillaume. “Efficient bounds for the softmax function, applications to inference in hybrid models.” 2007.

Chicago

Bouchard, Guillaume. 2007. “Efficient bounds for the softmax function, applications to inference in hybrid models.”

Harvard

Bouchard, G. (2007) ‘Efficient bounds for the softmax function, applications to inference in hybrid models’.

BibTeX

@article{bouchard2007efficient,
  title = {{Efficient bounds for the softmax function, applications to inference in hybrid models}},
  author = {Bouchard, Guillaume},
  year = {2007}
}

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