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Stopping Criteria in Contrastive Divergence: Alternatives to the Reconstruction Error
[article]
2014
arXiv
pre-print
Restricted Boltzmann Machines (RBMs) are general unsupervised learning devices to ascertain generative models of data distributions. RBMs are often trained using the Contrastive Divergence learning algorithm (CD), an approximation to the gradient of the data log-likelihood. A simple reconstruction error is often used to decide whether the approximation provided by the CD algorithm is good enough, though several authors (Schulz et al., 2010; Fischer & Igel, 2010) have raised doubts concerning
arXiv:1312.6062v2
fatcat:kfhe7kfnwffiln7ryuxbb5uuru