134 citations · 892 across the 40 of their papers we have counts for
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Efficient Evaluation of the Partition Function of RBMs with Annealed Importance Sampling
Ferran Mazzanti, Enrique Romero
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…
Weighted Contrastive Divergence
Enrique Romero Merino, Ferran Mazzanti Castrillejo, Jordi Delgado Pin +1
Learning algorithms for energy based Boltzmann architectures that rely on gradient descent are in general computationally prohibitive, typically due to the exponential number of te…
Stopping Criteria in Contrastive Divergence: Alternatives to the Reconstruction Error
David Buchaca, Enrique Romero, Ferran Mazzanti +1
Restricted Boltzmann Machines (RBMs) are general unsupervised learning devices to ascertain generative models of data distributions. RBMs are often trained using the Contrastive Di…