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Found 3 papers, 1 papers with code
Efficient Evaluation of the Partition Function of RBMs with Annealed Importance Sampling
Probabilistic models based on Restricted Boltzmann Machines (RBMs) imply the evaluation of normalized Boltzmann factors, which in turn require from the evaluation of the partition function Z.
On the use of Pairwise Distance Learning for Brain Signal Classification with Limited Observations
The increasing access to brain signal data using electroencephalography creates new opportunities to study electrophysiological brain activity and perform ambulatory diagnoses of neuronal diseases.
Stopping Criteria in Contrastive Divergence: Alternatives to the Reconstruction Error
Restricted Boltzmann Machines (RBMs) are general unsupervised learning devices to ascertain generative models of data distributions.
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