Jan 30, 2013 · In this paper we use variational methods to approximate the stochastic distribution using multi-modal mixtures of factorized distributions. We ...
Boltzmann machines are undirected graph ical models with two-state stochastic vari ables, in which the logarithms of the clique.
In this paper we use variational methods to approximate the stochastic distribution using multi-modal mixtures of factorized distributions. We present results ...
Jul 24, 1998 · In this paper we use variational methods to approximate the stochastic distribution using multi-modal mixtures of factorized distributions. We ...
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Mixture representations for inference and learning in Boltzmann machines. N. D. Lawrence, C. M. Bishop and M. I. Jordan. In G. F. Cooper and S. Moral (Eds ...
Jun 2, 2023 · Boltzmann Machines are probabilistic generative models that are widely used in machine learning and artificial intelligence.
Can do inference by running forward-bacwkard on each mixture, fit model with EM. ... But not an efficient representation ... Restricted Boltzmann Machines. Learning ...
We present a new approximate inference algo- rithm for Deep Boltzmann Machines (DBM's), a generative model with many layers of hid- den variables.
The limitations of deterministic Boltzmann machine learning
www.semanticscholar.org › paper › The-...
Mixture Representations for Inference and Learning in Boltzmann Machines · Neil D. LawrenceCharles M. BishopMichael I. Jordan. Computer Science, Mathematics.
... mixtures of product distributions. Section 4 elaborates on distributed representations and inference functions represented by discrete RBMs (Proposition 9 ...