Abstract for ``Continuous sigmoidal belief networks trained using slice sampling''

Brendan J. Frey 1997. Continuous sigmoidal belief networks trained using slice sampling. In Advances in Neural Information Processing Systems 9. MIT Press: Cambridge, MA. Presented at the Neural Information Processing Systems Conference, Denver, Colorado, Dec. 1996.

Real-valued random hidden variables can be useful for modelling latent structure that explains correlations among observed variables. I propose a simple unit that adds zero-mean Gaussian noise to its input before passing it through a sigmoidal squashing function. Such units can produce a variety of useful behaviors, ranging from deterministic to binary stochastic to continuous stochastic. I show how "slice sampling" can be used for inference and learning in top-down networks of these units and demonstrate learning on two simple problems.

Compressed postscript, uncompressed postscript.

Back to Brendan Frey's home page.