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Abstract:
The nonnegative Boltzmann machine (NNBM) is a recurrent
neural network model that can describe multimodal nonnegative data.
Application of maximum likelihood estimation to this model gives a
learning rule that is analogous to the binary Boltzmann machine. We
examine the utility of the mean field approximation for the NNBM,
and describe how Monte Carlo sampling techniques can be used to
learn the parameters of the NNBM. Reflective slice sampling is
particularly well-suited for this distribution, and can efficiently
be implemented to sample the distribution. We illustrate learning
of the NNBM on a translationally invariant distribution, as well as
on a generative model for images of human faces.
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