Abstract
For neural networks with a wide class of weight priors, it can be shown that in the limit of an infinite number of hidden units, the prior over functions tends to a gaussian process. In this article, analytic forms are derived for the covariance function of the gaussian processes corresponding to networks with sigmoidal and gaussian hidden units. This allows predictions to be made efficiently using networks with an infinite number of hidden units and shows, somewhat paradoxically, that it may be easier to carry out Bayesian prediction with infinite networks rather than finite ones.
Original language | English |
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Pages (from-to) | 1203-1216 |
Number of pages | 14 |
Journal | Neural Computation |
Volume | 10 |
Issue number | 5 |
DOIs | |
Publication status | Published - Jul 1998 |
Bibliographical note
Copyright of the Massachusetts Institute of Technology Press (MIT Press)Keywords
- neural networks
- Gaussian process
- hidden units
- infinite networks
- finite networks